Complexity

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Complexity in Medical Informatics

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Research Article | Open Access

Volume 2018 |Article ID 9193248 | https://doi.org/10.1155/2018/9193248

Dandan Tang, Man Zhang, Jiabo Xu, Xueliang Zhang, Fang Yang, Huling Li, Li Feng, Kai Wang, Yujian Zheng, "Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015", Complexity, vol. 2018, Article ID 9193248, 17 pages, 2018. https://doi.org/10.1155/2018/9193248

Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015

Academic Editor: Panayiotis Vlamos
Received29 May 2018
Accepted17 Sep 2018
Published10 Dec 2018

Abstract

Objective. Urumqi is one of the key areas of HIV/AIDS infection in Xinjiang and in China. The AIDS epidemic is spreading from high-risk groups to the general population, and the situation is still very serious. The goal of this study was to use four data mining algorithms to establish the identification model of HIV infection and compare their predictive performance. Method. The data from the sentinel monitoring data of the three groups of high-risk groups (injecting drug users (IDU), men who have sex with men (MSM), and female sex workers (FSW)) in Urumqi from 2009 to 2015 included demographic characteristics, sex behavior, and serological detection results. Then we used age, marital status, education level, and other variables as input variables and whether to infect HIV as output variables to establish four prediction models for the three datasets. We also used confusion matrix, accuracy, sensitivity, specificity, precision, recall, and the area under the receiver operating characteristic (ROC) curve (AUC) to evaluate classification performance and analyzed the importance of predictive variables. Results. The final experimental results show that random forests algorithm obtains the best results, the diagnostic accuracy for random forests on MSM dataset is 94.4821%, 97.5136% on FSW dataset, and 94.6375% on IDU dataset. The k-nearest neighbors algorithm came out second, with 91.5258% diagnostic accuracy on MSM dataset, 96.3083% diagnostic accuracy on FSW dataset, and 90.8287% diagnostic accuracy on IDU dataset, followed by support vector machine (94.0182%, 98.0369%, and 91.3571%). The decision tree algorithm was the poorest among the four algorithms, with 79.1761% diagnostic accuracy on MSM dataset, 87.0283% diagnostic accuracy on FSW dataset, and 74.3879% accuracy on IDU. Conclusions. Data mining technology, as a new method of assisting disease screening and diagnosis, can help medical personnel to screen and diagnose AIDS rapidly from a large number of information.

1. Introduction

Acquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with a very high fatality rate caused by human immunodeficiency virus (HIV) [1]. It alters the immune system making people much more vulnerable to infections and diseases [2]. Up to now, the HIV/AIDS epidemic has been one of the most important and crucial public health problems facing both developed and developing nations. Since the first case of HIV infection of China discovered in 1985, the number of the infected patients has been increasing year by year. The spread trend of AIDS in China has not been fundamentally controlled; AIDS prevention and control situation in Xinjiang is even severer. Xinjiang Uygur Autonomous Region is one of the provinces hardest hit by AIDS in China. The first HIV/AIDS case in Xinjiang was reported in 1995. At the end of 2011, the cumulative total of HIV/AIDS cases reported in Xinjiang accounted for 7.7% of all cumulative total of HIV/AIDS cases in the country, ranking the fifth position in China [3]. The total number of HIV/AIDS reported cases from 2004 to 2015 had been accumulated to 14,696, and it accounted for 5.56% of the total number of AIDS patients reported in China. There were also 3830 people died of HIV, which took up 4.56% of the total death cases induced by AIDS. The reported AIDS cases increased from 20 to 1868 with the average annual growth rate of 28.74, and the reported deaths increased from 5 to 680 with the average annual growth rate of 28.74 in the past decades, which were higher than that of the national average annual growth level [4]. Urumqi, the capital of Xinjiang Uygur Autonomous Region, is one of the main districts of AIDS infection in Xinjiang, and its AIDS epidemic has been consistently high. The largest group of HIV infection is injecting drug users in Urumqi. But in the late 2011, the proportion of the sexual route of transmission of infection is more than the intravenous drug users sharing syringes; the infection became the first way. More and more sexual partners, men and men crowd into the spread of AIDS high-risk groups [5, 6]. The situation of stemming the spread of HIV in persons at high risk of exposure and blocking the AIDS epidemic moving from high-risk groups to the general population proliferation is still very flinty. Therefore, HIV infection continues to be a major global public health issue.

Data mining is a newly developing technology based on machine study in artificial intelligence and database, and it can be classified into two categories: unsupervised learning and supervised learning [7]. Data mining is the process of selecting, exploring, and modeling large amounts of data, which aims at discovering unknown patterns or relationships and infer prediction rules from the data [8]. In the recent years, great advancement has been achieved in the medical research of data mining. Studies have applied data mining to analyze volumes of data, explore unknown factors of disease, develop predictive models, and produce meaningful reports in different medical research fields [911]. In the new period, the study of prevention, diagnosis, and treatment of HIV disease entered a new phase. A lot of domestic and foreign researchers have done on using the data mining technology to discover the relationship of the AIDS patient’s potential factors and the result of treatment based on HIV surveillance data or comprehensive clinical data [12]. Oliveira et al. built multilayer artificial neural networks (MLP), naive Bayesian classifiers (NB), support vector machines (SVM), and the k-nearest neighbor algorithm (KNN) in order to identify the main factors influencing reporting delays of HIV-AIDS cases within the Portuguese surveillance system. The results of this study strongly suggested that MLP provided the best results, with a higher classification accuracy (approximately 63%), precision (approximately 76%), and recall (approximately 60%) [13]. Wang et al. had developed three computational modeling methods to predict virological response to therapy from HIV genotype and other clinical information. The comparison results showed that an artificial neural network (ANN) models were significantly inferior to random forests (RF) and support vector machines (SVM) [14]. Hai-Lei, et al. constructed a 133 HIV carriers forecasting model based on support vector machines (SVM), and the HIV carriers were found in the port of a province in China during the period of 2004–2009. The overall accuracy rate of forecasting model was 90.60%, and its sensibility and specificity were 90.29% and 90.90%, respectively [15]. Hailu compared the prediction of the different data mining technologies, which were used to develop the HIV testing prediction model. Four popular data mining algorithms (decision tree, naive Bayes, neural network, and logistic regression) were used to build the model that predicted whether an individual was being tested for HIV. The final experimentation results indicated that the decision tree (random tree algorithm) performed the best with an accuracy of 96% [16].

However, in previous studies, few researches considered the use of data mining methods to construct predictive mathematical models of AIDS high-risk group based on several potential risk factors for surveillance report data. This paper aims at using data mining technology to identify the main factors influencing on the status of AIDS high-risk group infection (including injecting drug user (IDU), female sex worker (FSW), and men who have sex with men (MSM)) on surveillance report data in Urumqi and compare the prediction power of the different forecast models based on data mining technology. In order to accomplish this objective, several data mining classification models were considered, namely, random forests (RF), support vector machine (SVM), k-nearest neighbors (KNN), and decision tree (DT), using a 10-fold cross-validation technique. The classification performance was evaluated in terms of a confusion matrix, accuracy, sensitivity, specificity, precision, recall, and AUC values of the receiver operating characteristic (ROC) curves.

2. Materials and Methods

2.1. Study Population

The target populations that met the inclusion criteria in this paper were selected from the data between 2009 and 2015 that the sentinel surveillance of CDC at all levels in Urumqi was reported to China CDC Information System. There are three populations at higher risk of HIV exposure that were considered, including FSW which was defined as women who engaged in commercial sex trade during the investigation; IDU was defined as who takes oral, inhaling, or injecting heroin, cocaine, opium, morphine, marijuana, k-powder, methamphetamine, ecstasy, leprosy, etc.; and MSM was defined as people who have had intercourse or oral sex in the past years.

2.2. Data Source

The data applied in this paper consisted of three datasets from the higher risk of HIV/AIDS exposure populations collected between 2009 and 2015 by the Urumqi CDC. The three datasets are FSW dataset that included 9090 FSWs and 53 attributes, MSM dataset that included 5304 MSM and 57 attributes, and IDU dataset that included 7337 IDUs and 56 attributes. The collected data had three core survey questionnaires: FSW questionnaire, MSM questionnaire, and IDU questionnaire. The survey items included demographic characteristics (age-at-birth, gender, marital status, nation, place of household registration and educational level, etc.), serological detection results (antibody detection of HIV, syphilis, and HCV), high-risk behaviors factors (drug abuse behavior and sexual behavior), and AIDS prevention strategies and measures (the awareness of AIDS/HIV prevention knowledge, the conditions of prevention, and intervention service and situation of test-accepting).

2.3. Data Preprocessing

Data preprocessing plays an important role in the data mining tasks. Data preprocessing contains many kinds of methods for different preprocessing purposes, including data cleaning, data transformation, and data reduction [17]. In this study, we have selected some appropriate methods to optimize the original dataset. First, the attributes unrelated to the data mining goal were removed in advance, such as questionnaire ID, investigation date, and area codes. And the attributes with a large number of missing values were also removed. Second, the data grouping technique was used to simplify the data mining task. In the multiple distinct values of some attributes, such as age, a numerical variable was discretize into different category groups based on WHO standard for age classification. Ethnicity, originally with 56 distinct values, were converted into three distinct categories according to the constituent ratio of different nationalities as Hans, Uygurs, and others. In addition, simple statistical computations were performed with the R language and software environment, version 3.4.3, to analyze the distribution of the attributes. The dependent variable (T03C) was a binary outcome variable of people who has been tested for HIV with two categories: 0 and 1, where 0 means the HIV test results were negative and 1 means the HIV test results were positive. The results of the attributes description are presented in Tables 1, 2, and 3.


VariablesDescriptionCategoryTotal numberPercentage (%)

A01BMonitoring sitesSayibak District298156.2
Xinshi District62411.8
Shuimogou District3616.8
Tianshan District133825.2
A06Sample sourceBar/dancehall/tearooms/club78314.8
Bath/sauna/pedicure/massage66912.6
Park/public toilet/grassland1883.5
Network recruiting360568.0
Others591.1
B01Age1(15–17)270.5
2(18–28)270451.0
3(29–40)214040.3
4(41–48)3626.8
5(49–55)591.1
6(56–65)80.2
7(>66)40.1
B02Marital statusUnmarried437782.5
Married60211.3
Cohabitation761.4
Divorced or widowed2494.7
B03The location of household registerXinjiang Uygur Autonomous Region462087.1
Others68412.9
B04NationHans454685.7
Uygurs3336.3
Others4258.0
B05Inhabit time<3 months1232.3
3–6 months591.1
7–12 months961.8
1–2 years3326.3
>2 years469488.5
B06Educational levelIlliteracy80.2
Primary school430.8
Junior middle school3466.5
High school or technical school108120.4
College or above382672.1
C08Knowledge and awareness of HIVNo1863.5
Yes511896.5
D01Have you ever had anal sex with a person of the same sex in the last six monthsNo3566.7
Yes494893.3
D03Did you use a condom for sex with the same sex last timeNo116922.0
Yes413578.0
E01Have you had any commercial sex with people of the same sex last 6 monthsNo502494.7
Yes2805.3
F01Did you have sex with the opposite sex last 6 monthsNo480190.5
Yes5039.5
G01Did you take drugsNo527099.4
Yes340.6
H01Have you ever been diagnosed with an STD in the last yearNo516897.4
Yes1362.6
I01Have you ever received a condom promotion or HIV/AIDS counselling and testing to prevent HIV/AIDSNo63311.9
Yes467188.1
I02Have you ever received a community medication to maintain or providing or exchanging cleaning needles to prevent HIV/AIDSNo526899.3
Yes360.7
I03Have you ever received a companion education to prevent HIV/AIDSNo170932.2
Yes359567.8
J01Has HIV been tested in the last yearNo172632.5
Yes357867.5
T04CSyphilis test resultsNo497993.9
Yes3256.1
T05CHepatitis test resultsYes390.7
No526599.3
T03CHIV test resultsNo492792.9
Yes3777.1


VariablesDescriptionCategoryTotal numberPercentage (%)

A01BMonitoring sitesSayibak District255728.1
Xinshi District109912.1
Economic Development District5225.7
Shuimogou District265329.2
Tianshan District225924.9
A06Sample sourceSauna/bath center7788.6
Nightclub315734.7
Karaoke hall/ballroom/bar238826.3
Guesthouse/hotel5516.1
Foot washing room/hair salon155117.1
Roadside shop/little dine6567.2
Street90.1
B01BAge1(15–17)1091.2
2(18–28)647971.3
3(29–40)202822.3
4(41–48)3944.3
5(49–55)660.7
6(56–65)70.1
B02Marital statusUnmarried528858.2
Married235926.0
Cohabitation105111.6
Divorced or widowed3924.3
B03The location of household registerXinjiang Uygur Autonomous Region494754.4
Others414345.6
B04NationHans740581.5
Uygurs7858.6
Others9009.9
B05Educational levelIlliteracy1181.3
Primary school94910.4
Junior middle school373841.1
High school or technical school338337.2
College or above9029.9
B06How long were you working here this time>=1 year318035.0
6–12 months193021.1
1–6 months277330.5
<1 months120713.3
C08Knowledge and awareness of HIVNo4014.4
Yes868995.6
D01Did you use condoms with your guests the last timeNo93210.3
Yes815889.7
D02How often did you use condoms when you have sex with a guest last monthNever used1902.1
Sometimes used216023.8
Every time used674074.1
E01Did you take drugsNo902999.3
Yes610.7
F01Have you ever been diagnosed with an STD in the last yearNo906099.7
Yes300.3
G01Have you ever received a condom promotion or HIV/AIDS counselling and testing to prevent HIV/AIDSNo5045.5
Yes858694.5
G02Have you ever received a community medication to maintain or providing or exchanging cleaning needles to prevent HIV/AIDSNo895098.5
Yes1401.5
G03Have you ever received a companion education to prevent HIV/AIDSNo248527.0
Yes663273.0
H01Has HIV been tested in the last yearNo442948.7
Yes466151.3
T04CSyphilis test resultsNo890498.0
Yes1862.0
T05CHepatitis test resultsNo898698.9
Yes1041.1
T03CHIV test resultsNo904199.5
Yes490.5


VariablesDescriptionCategoryTotal numberPercentage (%)

A01BMonitoring sitesSayibak District214732.9
Xinshi District89212.2
Shuimogou District180224.6
Tianshan District192226.2
Toutun River District560.8
Urumqi County2483.4
A06Sample sourceCompulsory detoxification setting161722.0
Community506369.0
Methadone clinic (urine test positive)6579.0
B02Age1(15–17)490.7
2(18–28)171923.4
3(29–40)349347.6
4(41–48)172123.5
5(49–55)3054.2
6(56–65)430.6
7(>66)50.1
B01GenderMale654989.3
Female78810.7
B03Marital statusUnmarried258635.2
Married324144.2
Cohabitation2253.1
Divorced or widowed128517.5
B04The location of household registerXinjiang Uygur Autonomous Region676292.2
Others5757.8
B05NationHans245233.4
Uygurs388052.9
Others100513.7
B06Educational levelIlliteracy3775.1
Primary school156121.3
Junior middle school323144.0
High school or technical school167322.8
College or above4956.7
C08Knowledge and awareness of HIVNo1391.9
Yes719898.1
D01How many drugs did you use at present1 kind661890.2
2 kinds6468.8
3 kinds570.8
4 kinds130.2
5 kinds20.0
6 kinds10.0
Sometimes used216023.8
Every time used674074.1
D02Did you take drugsNo181224.7
Yes552575.3
E01Have you ever had sex last monthNo456262.2
Yes277537.8
F01Have you ever had sex with a commercial partner in the last yearNo651688.8
Yes82111.2
G01Have you ever received a condom promotion or HIV/AIDS counselling and testing to prevent HIV/AIDSNo145319.8
Yes588480.2
G02Have you ever received a community medication to maintain or providing or exchanging cleaning needles to prevent HIV/AIDSNo305641.7
Yes428158.3
G03Have you ever received a companion education to prevent HIV/AIDSNo335345.7
Yes398454.3
H01Has HIV been tested in the last yearNo308042.0
Yes425758.0
T04CSyphilis test resultsNo709396.7
Yes2443.3
T05CHepatitis test resultsNo338946.2
Yes394853.8
T03CHIV test resultsNo608783.0
Yes125017.0

Table 1 shows a total of 5304 MSM respondents tested for HIV. Among them, 377 (7.11%) were detected as HIV positive and 4927 (92.9%) were detected as HIV negative. Table 2 shows a total of 9090 FSW respondents who had received a HIV test; 9041 (99.5%) were HIV-positive, while only 49 (0.5%) were HIV negative. Table 3 shows 7337 IDU respondents who had accepted a HIV test; the HIV negative and positive were 6087 (83%) and 1250 (17%), respectively. These results indicate that there is a need of balancing these two classes of the three datasets. In this article, we employed the Synthetic Minority Over-sampling Technique (SMOTE) [18] to dispose unbalanced samples. In SMOTE algorithm, majority class samples use the undersampling method and minority class samples use the oversampling technique. It potentially performs better than simple oversampling and it is widely used [19, 20].

2.4. Attribute Selection

In a data mining task, the selection of the input attributes is usually a highly important step to improve the classification ability of the models, to reduce the classier complexity, to save the computational time, and to simplify the obtained results. Filtering and wrapper are two main different approaches to select a subset of attributes from all of the attributes used in machine learning. Filtering is to make an independent assessment based on the data general characteristics. Wrapper is to select a feature subset using the evaluation function based on a machine learning algorithm [21]. In this paper, the wrapper methods based on random forests (RF) was used to select the attributes as the inputs of the classification model. RF algorithm is an ensemble learning method based on the aggregation of a large number of decision trees and has proved to be very powerful in many different applications [2224]. A feature selection based on the random forest classifier has been found to provide multivariate feature importance scores, which are relatively easy to obtain and have been successfully applied to high dimensional data [25, 26]. The quantification procedures of the variable importance scores can be described as follows: computing the variable importance score and permuting score, then selecting the features that have more contribution to classification model, and building models through the feature evaluation criteria of random forest algorithm. The Gini importance considers conditional higher-order interactions among the variables and might be a preferable ranking criterion than a univariate measure [27, 28] and is the feature importance evaluation criteria of random forest algorithm which was used in this study.

2.5. Classification Models
2.5.1. Random Forests (RF)

The first algorithm for random decision forests was created by Ho (1995) [29], and its extension version was developed by Breiman [30]. The RF is an ensemble learning method based on decision tree and has been successfully used in several types of classification and regression, especially for accurate identification of disease diagnosis problems [3133]. RF builds a large number of decision trees using a bootstrap sample with replacement from the training set and predicts the class of each tree according to the test set, and the final RF prediction class is presented based on the majority of the votes [34]. It has been shown to give excellent performance on numerical and categorical data.

2.5.2. Support Vector Machine (SVM)

Support vector machine, a novel type of learning machine derived from statistical learning theory, constructs a hyperplane or set of hyperplanes in high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection, function estimation, and high-dimensional pattern recognition problems [3538]. The SVM mainly deals with the problems of binary classification. In addition to performing linear classification, SVM can efficiently perform nonlinear classification through kernel techniques [39] implicitly mapping their inputs into high-dimensional feature spaces. SVM categorization model can be constructed in two ways, as follows: (1) converting the input space into higher dimensional feature space by a nonlinear mapping function. (2) Building the separating hyperplane based on maximum distance from the closest points of the training set [40].

2.5.3. K-Nearest Neighbors (KNN)

The k-nearest neighbors algorithm (KNN) is the simplest but more powerful nonparametric classification method of all data mining methods, since it is a type of instance-based or lazy learning algorithm [41]. KNN classifier has been widely used in many fields, such as text classification, pattern recognition, and disease detection and diagnosis, based on the advantages such as simplicity, high efficiency, and easy to implement [42, 43]. KNN arithmetic idea mainly considers three points: the value of k, distance measurement, and decision rules of classification. The k, as a user-defined constant, will directly affect the KNN classification performance. And the distance metric measures commonly use Euclidean distance, Manhattan distance, and Minkowski distance. The decision rules of classification depend on the majority voting.

2.5.4. Decision Trees (DT)

A decision tree is a kind of commonly used data mining method with many advantages such as easy to understand, readable, and quick classification [44]. A decision tree is the organization of the nodes that make decisions like a tree, which consists of decision nodes, branches, and leaf nodes. Each decision node represents a data category or attributes to be classified, and each leaf node represents a result [45]. The whole decision-making process starts from the root decision node, and from top to bottom, it is determined until the classification results are given. There are three commonly used typical decision tree algorithms in data mining at present, such as ID3 algorithm, C4.5 algorithm, and CART algorithm [46].

2.6. Performance Evaluation

In this paper, a confusion matrix and some indicators including accuracy, sensitivity, specificity, precision, recall, and the receiver operating characteristic (ROC) curve were used to appraise the performance of the four classification models. A 10-fold cross-validation was applied to RF, SVM, KNN, and DT validation. A confusion matrix consists of the parts shown in Table 4. In Table 4, TP (true positive) is the positive records of the correct classification, TN (true negative) is the negative records of the correct classification, FP (false positive) is the positive records of the incorrect classification, and FN (false negative) is the negative records of the incorrect classification.


Predicted negativePredicted positive

Actual negativeTNFP
Actual positiveFNTP

Several important measures, such as accuracy, sensitivity, specificity, precision, and recall, can be calculated by using the confusion matrix. The accuracy is the number of samples correctly classified. The sensitivity is a description of measuring the proportion of correctly classified positive samples. The specificity is a description of measuring the proportion of correctly classified negative samples. The precision is a description of the number of positive samples to the proportion of all predicted positive samples. The recall is a description of the ratio of positive samples to the total number of positive samples. The accuracy, sensitivity, specificity, precision, and recall are defined as follows:

The ROC curve is originally derived from statistical decision theory, which can comprehensively describe the classification performance of the classifiers with different discriminant thresholds [47]. The vertical axis of the ROC curve is TP rate, and the horizontal axis is FP rate. However, in a practical application, the AUC (the area under the ROC curve) is often used to evaluate the performance of the classifier.

3. Experimental Results

R is an open source programming language and software environment for statistical computing and graphics. Based on the R language environment, the implementation of each algorithm in this experiment is carried out. Here, we used SMOTE (DMwR), randomForest (randomForest), ksvm (kernlab), kknn (kknn), and rpart (rpart) packages. All experiments were validated with a 10-fold cross-validation technique in order to present a more stable accuracy rate after applying the four classification models. Some evaluation indexes were used to compare the classification performance of four data mining algorithms.

Table 5 shows the three original datasets and the three artificial datasets obtained using SMOTE algorithm. It is evident that the original datasets are biased; the imbalance rate of each original datasets is 13.0689, 184.5102, and 4.8696, respectively. In order to achieve the data balance to avoid the result bias, we used SMOTE algorithm combining the oversampling the minority class and undersampling the majority class techniques. We apply the function SMOTE in the DMwR package in R software. The three main parameters of function SMOTE are perc.over, perc.under, and k. The parameter perc.over and perc.under control the amount of oversampling of the minority classes and undersampling of the majority classes, respectively. The parameter k controls the way of the new examples created. For the parameters in the SMOTE algorithm, the value of k was set to 5. For the initial dataset of MSM with 377 minority samples and 4927 majority samples, we set the parameters perc.over = 1200 and perc.under = 110, respectively. Firstly, the number of minority samples was increased; a total of 1200 × 377/100 new minority samples were generated. The original minority samples and the new minority samples consisted of the new dataset. Secondly, sampling the majority sample, we obtain a new sample of the majority, which is (110/100) × 1200 × 377/100. We put the new sample of the majority into the new dataset which was created above. Eventually, in this new dataset, both the minority sample and the majority sample were (1 + 1200/100) × 377 and (110/100) × 1200 × 377/100, respectively. For the initial dataset of FSW with 49 minority samples and 9041 majority samples, we set the parameters perc.over = 20,000 and perc.under = 101. The oversampling and undersampling algorithms also were utilized in the MSM dataset. The result demonstrated the new dataset with minority samples (1 + 20,000/100) × 49 and majority samples (101/100) × 20,000/49/100. For the initial dataset of IDU with 1250 minority samples and 6087 majority samples, setting the parameters perc.over = 400 and perc.under = 216, the minority sample and the majority sample were 1 + 400/100 × 1250 and 216/100 × 400 × 1250/100, respectively.


DatasetMinority classMajority classSamples in totalImbalance rate

MSM (original)3774927530413.0689
MSM (SMOTE)4901497698771.0153
FSW (original)4990419090184.5102
FSW (SMOTE)9849989819,7471.0049
IDU (original)1250608773374.8696
IDU (SMOTE)6250630012,5501.008

Figures 1, 2, and 3 describe the importance of the sorted variables of the three datasets (MSM dataset, FSW dataset, and IDU dataset) according to the Gini index criterion from RF. From Figure 1, for the MSM dataset, the most important variables are B01, B06, A01B, A06, and B05. The least important variables are I02, G01, H01, and D01. From Figure 2, for the FSW dataset, the most important variables are B01B, T05C, A06, B05, and B06. The least important variables are F01, G02, C08, E01, and D01. From Figure 3, for the IDU dataset, the most important variables are B02, A01, T05C, B06, and B05. The least important variables are C08, B04, T04C, F01, and D01. Finally, applying the rank + MeanDecreaseGini method of attribute selection method, variables were ranked based on their importance in classifying the HIV patients. We also asked the CDC doctors about the importance of lower-ranking attributes, combining the two methods agree that B01, B06, A01B, A06, B05, B04, B02, D03, I03, J01, I01, B03, F01, T04C, and E01 as the main subset of attributes important in predicting the HIV patients from MSM population, B01B, T05C, A06, B05, B06, B04, B02, A01B, D02, H01, T04C, G03, B03, and G01 as the main subset of attributes important in predicting the HIV patients from female sex workers population, and B02, A01, T05C, B06, B05, B03, A06, D02, H01, G02, G03, E01, G01, and B01 as the main subset of attributes important in predicting the HIV patients from drug users population. The detailed descriptions of the selected attributes were shown in Tables 6, 7, and 8.


RankAttributeMeanDecreaseGini

1B01: age76.8033
2B06: educational level27.1032
3A01B: monitoring sites26.0119
4A06: sample source23.9942
5B05: inhabit time18.3735
6B04: nation16.2218
7B02: marital status14.9883
8D03: did you use a condom for sex with the same sex last time12.7123
9I03: have you ever received a companion education to prevent HIV/AIDS12.2440
10J01: has HIV been tested in the last year12.1464
11I01: have you ever received a condom promotion or HIV/AIDS counselling and testing to prevent HIV/AIDS10.1819
12B03: the location of household register9.7513
13F01: did you have sex with the opposite sex last 6 months8.5185
14T04C: syphilis test results8.2889
15E01: have you had any commercial sex with people of the same sex last 6 months7.6851


RankVariablesMeanDecreaseGini

1B01B: age12.6253
2T05C: hepatitis test results6.7033
3A06: sample source6.6001
4B05: educational level6.3421
5B06: how long were you working here this time6.1513
6B04: nation5.2128
7B02: marital status4.6192
8A01B: monitoring sites4.4660
9D02: how often did you use condoms when you have sex with a guest last month2.9029
10H01: has HIV been tested in the last year2.8776
11T04C: syphilis test results2.8470
12G03: have you ever received a companion education to prevent HIV/AIDS?1.6805
13B03: the location of household register1.4158
14G01: have you ever received a condom promotion or HIV/AIDS counselling and testing to prevent HIV/AIDS1.2143


RankVariablesMeanDecreaseGini

1B02: age292.3608
2A01: monitoring sites166.8695
3T05C: hepatitis test results142.0867
4B06: educational level125.3663
5B05: nation112.2430
6B03: marital status92.2254
7A06: sample source63.6016
8D02: have you ever injected drugs58.3517
9H01: has HIV been tested in the last year55.1894
10G02: have you ever received a community medication to maintain or providing or exchanging cleaning needles to prevent HIV/AIDS45.0500
11G03: have you ever received a companion education to prevent HIV/AIDS44.9624
12E01: have you ever had sex last month43.5729
13G01: have you ever received a condom promotion or HIV/AIDS counselling and testing to prevent HIV/AIDS42.9014
14B01: gender33.0323

Figures 4, 5, and 6 show the ROC curve obtained for the three datasets with the four classifiers. The AUC scores for RF, SVM, KNN, and DT on MSM dataset are 0.9802, 0.9401, 0.9747, and 0.7917; 0.9981, 0.9803, 0.9967, and 0.8702 on FSW dataset; and 0.9874, 0.9135, 0.9802, and 0.7438 on IDU dataset. It is obvious that RF performed significantly better than the other three classifiers. The AUC scores achieved for MSM dataset, FSW dataset, and IDU datasets are 0.9802, 0.9981, and 0.9874, respectively. The maximum value of the AUC (0.9981) was obtained for the FSW dataset with RF algorithm. Moreover, the value of AUC of DT algorithm with IDU dataset is 0.7438 which is the minimum of all AUC scores.

Figures 7, 8, and 9 depict the classification performance when the four classifiers are applied on MSM dataset, FSW dataset, and IDU dataset, respectively. The accuracy, precision, and recall for RF, SVM, KNN, and DT on the three datasets were compared. For the MSM dataset (Figure 7), the SVM model achieved a classification accuracy of 87.8404%, with a precision of 89.5130% and a recall of 85.5132%. The KNN model had a classification accuracy of 91.5258%, with a precision of 89.5130% and a recall of 85.5132%. For the decision tree, the accuracy, precision, and recall were 76.7440%, 77.6199%, and 74.6582%, respectively. The random forest algorithm performed best among the four evaluated models with an accuracy of 94.4821%, a precision of 98.5511%, and a recall of 90.2061%.

For the FSW dataset (Figure 8), the final experimental results demonstrated that the random forest algorithm showed the best with an accuracy of 97.5136%, and the precision and recall were 97.4638% and 91.6160%, respectively. The KNN model came out to be the second with a classification accuracy of 96.3083%, and the precision and recall were 97.4210% and 95.1163%, respectively, followed by SVM model with a classification accuracy of 93.3560%, the precision and recall equal to 94.1554% and 92.4155%, respectively. The decision tree has also performed the least classification accuracy of 85.0408%, and the precision and recall were 86.9467% and 82.3739%, respectively.

For the IDU dataset (Figure 9), the RF classifier showed the best predictive performances; the accuracy, precision, and recall gave 94.6375%, 97.4638%, and 91.6160%, respectively. In the SVM model, they were 83.4821%, 84.8141%, and 81.4080%, respectively. As shown in the confusion matrix in Table 10, the KNN learning algorithm scored an accuracy of 90.8287%; the precision and recall were 94.7831%, 86.3360%, respectively. Using the decision tree had a lower overall performance, with an accuracy of 71.2271%, precision and recall were 69.8690% and 74.2400%, respectively.

The other performance metrics confusion matrixes, such as sensitivity and specificity, were also employed to measure the performance of different classifiers for the three datasets. As a whole, the RF classifier has the best performance as compared to the other three methods and has obtained higher accuracies 94.4821%, 97.5136%, and 94.6375% on MSM dataset, FSW dataset, and IDU dataset, respectively. The decision tree has also achieved the least classification accuracy 76.7440%, 85.0408%, and 71.2271% on MSM dataset, FSW dataset, and IDU dataset, respectively. The detailed classification outcomes of each model for the three datasets are shown in Tables 9, 10, and 11.


Testing criteriaRFSVMKNNDT

Confusion matrix4911654485491482814839211055
48044217104191689421212423659
Accuracy (%)94.482187.840491.525876.7440
Sensitivity (%)90.206185.513285.941674.6582
Specificity (%)98.693790.132697.025778.7982
Precision (%)98.551189.513096.605577.6199
Recall (%)90.206185.513285.941674.6582
AUC (%)98.021794.018297.470979.1761


Testing criteriaRFSVMKNNDT

Confusion matrix61511495389911600329742992001
524572611625088854539616104640
Accuracy (%)94.637583.482190.828771.2271
Sensitivity (%)91.616081.408086.336074.2400
Specificity (%)97.634985.539795.285768.2381
Precision (%)97.463884.814194.783169.8690
Recall (%)91.616081.408086.336074.2400
AUC (%)98.749591.357198.020874.3879


Testing criteriaRFSVMKNNDT

Confusion matrix97091899333565965024886801218
30295477479102481936817368113
Accuracy (%)97.513693.356096.308385.0408
Sensitivity (%)96.933792.415595.116382.3739
Specificity (%)98.090594.291897.494487.6945
Precision (%)98.058894.155497.421086.9467
Recall (%)96.933792.415595.116382.3739
AUC (%)99.811498.036999.671287.0283

4. Discussion

The AIDS epidemic in Urumqi is still very serious. The increasing number of high-risk groups, such as prostitutes, male sex workers, and floating population, has exacerbated the difficulty of AIDS prevention and treatment. Data mining has been widely used in the field of diagnosis, evaluation, and other medical fields [48]. This study aimed at using four mature data mining algorithms (random forests, support vector machine, k-nearest neighbors, and decision tree) to build identification models for AIDS patients based on the sentinel monitoring data of HIV high-risk populations (MSM, FSWs, and IDUs) in Urumqi and compared the prediction power of the different models. However, considering that the major defect in the model build process is class imbalances, the SMOTE method has been used to simulate the data balance and overcome the problem of overfitting according to the previous research [49].

For all datasets, the final experimental results showed that RF algorithm obtains the best results; the diagnostic accuracy for RF on MSM dataset are 94.4821%, 97.5136% on FSW dataset, and 94.6375% on IDU dataset. The KNN algorithm came out second, with 91.5258% diagnostic accuracy on MSM dataset, 96.3083% diagnostic accuracy on FSW dataset, and 90.8287% diagnostic accuracy on IDU dataset, followed by SVM (94.0182%, 98.0369%, and 91.3571%). The DT algorithm was the poorest of the four algorithms, with 79.1761% diagnostic accuracy on MSM dataset, 87.0283% diagnostic accuracy on FSW dataset, and 74.3879% accuracy on IDU. These results suggested that the four established data mining models can predict whether a person is infected with HIV. But compared with SVM, decision tree, and KNN, random forest model through a large number of random sample method balance the sampling error; the effect of classifying the results produces a large number of different test data. A comprehensive assessment is just a single test sample for fitting the results of the other three models more reliably [50].

This study based on the importance score of independent variables for random forest model identified the most important influencing factor for the HIV infection in the three high dangerous populations in Urumqi. For the MSM dataset, these variables are age, educational level, monitoring sites, sample source, inhabit time, nation, marital status, etc. Variables such as age show that the MSM population in Urumqi is mainly the young and middle-aged active population aged from 18 to 40 years old, accounting for 91.3%, which is similar to the monitoring results in Chengdu [51] and show that sexually active people are still the focus of AIDS prevention and treatment. The majority (82.5%) of the participants had never been married. More than half (56.2%) came from the Sayibak District, 68% of the participants were recruited through the network, and 72.1% had some college or higher education. Therefore, based on the epidemic characteristics of MSM population in Urumqi, personal characteristics and social factors should be taken into account comprehensively when education intervention measures are carried out for this population. For the FSW dataset, the results showed that most of the female sex workers (FSWs) in Urumqi were young women under 30 years old, 58.2% were unmarried, 65% of female sex workers (FSWs) worked in a local workspace for less than a year, and more than half were primary school and junior middle school and had come mainly from nightclub, karaoke, ballroom, and bar. Therefore, we should focus on the actual epidemic characteristics of FSWs to take corresponding measures to publicize education and intervene. For the IDU dataset, the age of the 7337 participants ranged from l1 to 71 years, with more than half (94.5%) of them aged 18–48 years. Among them, 2586 (35.2%) were single, with 2147 (32.9%) participants coming from Sayibak District, and 5169(66.4%) participants were junior high school and below. Among the participants, 89.3% were male and 69% were from the community. These results can provide evidence for the prevention of HIV infection among drug users through the promotion of education, especially for adolescents, low cultural level population, floating population, drug abuse, sexual disorder, etc.

As we have shown above, data mining models can accurately identify diseases based on certain important attributes. These predictive models are valuable tools in the medical field. However, there are areas of concern in the development of predictive models: (1) the model should include all clinically relevant data, (2) the model should be tested on an independent sample, and (3) the model must make sense to the medical personnel who are supposed to make use of it. It has been shown that not all predictive models constructed using data mining techniques satisfy all of these requirements [52].

There are some limitations to this article. First, all individuals are recruited in Urumqi, which was limited by geographical and population characteristics. Therefore, the information bias may exist during the experiment process. If the study population could be expanded to more than one province or to the whole country, the model recognition effect would be better. Second, in the epidemiological investigation of HIV-infected persons, due to subjective, objective, and other reasons, respondents may provide unreal information, which leads to a certain influence on the analysis results. In the future, more feature selection methods, class imbalance processing methods, and data mining algorithms are expected to be tested.

5. Conclusion

In general, four prediction models were established and compared for predicting whether a person is infected with HIV. The results showed that the random forest model performed the best in classification accuracy. This study can provide some effective ways for medical staffs to quickly screen and diagnose AIDS from a large amount of information.

Data Availability

The (CSV) data used to support the findings of this study are restricted in order to protect patient privacy. Data are available from Kai Wang (wangkaimath@sina.com) for researchers who meet the criteria for access to confidential data.

Conflicts of Interest

The authors declare no conflict of interest.

Authors’ Contributions

Dandan Tang, Kai Wang, and Yujian Zheng designed the project; Man Zhang, Jiabo Xu, and Xueliang Zhang participated in the data collection; Dandan Tang, Li Feng, and Huling Li performed the analysis of the data; Dandan Tang and Fang Yang wrote the manuscript. All authors contributed to the interpretation of the results, revised the manuscript critically, and approved the final version of the manuscript.

Acknowledgments

This project was supported by the National Natural Science Foundation of China (11461073, 11301451).

References

  1. O. Singh and E. C. Y. Su, “Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features,” BMC Bioinformatics, vol. 17, Supplement 17, pp. 478–289, 2016. View at: Publisher Site | Google Scholar
  2. M. A. Nowak and A. J. Mcmichael, “How HIV defeats the immune system,” Scientific American, vol. 273, no. 2, pp. 58–65, 1995. View at: Publisher Site | Google Scholar
  3. N. I. Ming-Jian, J. Chen, Y. Zhang et al., “Analysis of epidemic status of HIV/AIDS in Xinjiang,” Bulletin of Disease Control and Prevention, vol. 27, no. 2, pp. 1–3, 2012. View at: Google Scholar
  4. Q. Zheng, J. Wang, Y. Dong et al., “Analysis of monitoring data of AIDS in Xinjiang from 2004 to 2015,” Bulletin of Disease Control & Prevention, vol. 32, no. 1, pp. 34–48, 2017. View at: Google Scholar
  5. M. A. Ling, “HIV/AIDS epidemic in Urumqi from 1995 to 2011,” Modern Preventive Medicine, vol. 109, pp. 2727–2729, 2013. View at: Google Scholar
  6. M. A. Ling and Y. X. Wang, “Characteristics of man who have sex with men HIV/AIDS cases reported through internet based direct reporting system in Urumqi,” World Latest Medicine Information, vol. 16, no. 52, pp. 1-2, 2016. View at: Google Scholar
  7. I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Google Ebook, 2011.
  8. D. A. Adeniyi, Z. Wei, and Y. Yongquan, “Automated web usage data mining and recommendation system using k-nearest neighbor (knn) classification method,” Applied Computing and Informatics, vol. 12, no. 1, pp. 90–108, 2016. View at: Publisher Site | Google Scholar
  9. H. B. Burke, P. H. Goodman, D. B. Rosen et al., “Artificial neural networks improve the accuracy of cancer survival prediction,” Cancer, vol. 79, pp. 857–862, 1997. View at: Publisher Site | Google Scholar
  10. C. D. Chang, C. C. Wang, and B. C. Jiang, “Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors,” Expert Systems with Applications, vol. 38, no. 5, pp. 5507–5513, 2011. View at: Publisher Site | Google Scholar
  11. X. H. Meng, Y. X. Huang, D. P. Rao, Q. Zhang, and Q. Liu, “Comparison of three data mining models for predicting diabetes or prediabetes by risk factors,” The Kaohsiung Journal of Medical Sciences, vol. 29, no. 2, pp. 93–99, 2013. View at: Publisher Site | Google Scholar
  12. L. Wang, “Application of data mining technology in diagnosis and treatment of AIDS,” Journal of Mathematical Medicine, vol. 26, no. 1, pp. 97–99, 2013. View at: Google Scholar
  13. A. Oliveira, B. M. Faria, A. R. Gaio, and L. P. Reis, “Data mining in HIV-AIDS surveillance system,” Journal of Medical Systems, vol. 41, no. 4, p. 51, 2017. View at: Publisher Site | Google Scholar
  14. D. Wang, B. Larder, A. Revell et al., “A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy,” Artificial Intelligence in Medicine, vol. 47, no. 1, pp. 63–74, 2009. View at: Publisher Site | Google Scholar
  15. W. U. Hai-Lei, J. S. Qian, and C. Zhang, “A HIV carrier forecasting model for quarantine based on support vector machines,” Practical Preventive Medicine, vol. 17, no. 11, pp. 2152–2155, 2010. View at: Google Scholar
  16. T. G. Hailu, “Comparing data mining techniques in HIV testing prediction,” Intelligent Information Management, vol. 07, no. 3, pp. 153–180, 2015. View at: Publisher Site | Google Scholar
  17. A. Famili, W. Shen, R. Weber, and E. Simoudis, “Data preprocessing and intelligent data analysis,” Intelligent Data Analysis, vol. 1, no. 1-4, pp. 3–23, 1997. View at: Publisher Site | Google Scholar
  18. R. Blagus and L. Lusa, “SMOTE for high-dimensional class-imbalanced data,” BMC Bioinformatics, vol. 14, no. 1, pp. 106–116, 2013. View at: Publisher Site | Google Scholar
  19. L. Zhang, C. Zhang, R. Gao, R. Yang, and Q. Song, “Using the SMOTE technique and hybrid features to predict the types of ion channel-targeted conotoxins,” Journal of Theoretical Biology, vol. 403, pp. 75–84, 2016. View at: Publisher Site | Google Scholar
  20. E. M. Karabulut and T. Ibrikci, “Effective automated prediction of vertebral column pathologies based on logistic model tree with smote preprocessing,” Journal of Medical Systems, vol. 38, no. 5, p. 50, 2014. View at: Publisher Site | Google Scholar
  21. H. Liu, X. Shi, D. Guo, Z. Zhao, and Yimin, “Feature selection combined with neural network structure optimization for HIV-1 protease cleavage site prediction,” BioMed Research International, vol. 2015, Article ID 263586, 11 pages, 2015. View at: Publisher Site | Google Scholar
  22. J. R. Bienkowska, G. S. Dalgin, F. Batliwalla et al., “Convergent random forest predictor: methodology for predicting drug response from genome-scale data applied to anti-TNF response,” Genomics, vol. 94, no. 6, pp. 423–432, 2009. View at: Publisher Site | Google Scholar
  23. X. Chen and H. Ishwaran, “Random forests for genomic data analysis,” Genomics, vol. 99, no. 6, pp. 323–329, 2012. View at: Publisher Site | Google Scholar
  24. M. Kotti, L. D. Duffell, A. A. Faisal, and A. H. Mcgregor, “Detecting knee osteoarthritis and its discriminating parameters using random forests,” Medical Engineering & Physics, vol. 43, pp. 19–29, 2017. View at: Publisher Site | Google Scholar
  25. A. Hapfelmeier and K. Ulm, “A new variable selection approach using random forests,” Computational Statistics & Data Analysis, vol. 60, pp. 50–69, 2013. View at: Publisher Site | Google Scholar
  26. M. Sandri and P. Zuccolotto, “Variable selection using random forests,” in Data Analysis, Classification and the Forward Search, pp. 263–270, 2006. View at: Publisher Site | Google Scholar
  27. B. H. Menze, B. M. Kelm, R. Masuch et al., “A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data,” BMC Bioinformatics, vol. 10, no. 1, pp. 213–216, 2009. View at: Publisher Site | Google Scholar
  28. A.-L. Boulesteix, A. Bender, J. Lorenzo Bermejo, and C. Strobl, “Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations,” Briefings in Bioinformatics, vol. 13, no. 3, pp. 292–304, 2012. View at: Publisher Site | Google Scholar
  29. T. K. Ho, “Random decision forests,” in Proceedings of 3rd International Conference on Document Analysis and Recognition, p. 278, Montreal, Quebec, Canada, August 1995. View at: Publisher Site | Google Scholar
  30. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at: Publisher Site | Google Scholar
  31. M. Dauwan, J. J. van der Zande, E. van Dellen et al., “Random forest to differentiate dementia with Lewy bodies from Alzheimer’s disease,” Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, vol. 4, pp. 99–106, 2016. View at: Publisher Site | Google Scholar
  32. A. T. Azar, H. I. Elshazly, A. E. Hassanien, and A. M. Elkorany, “A random forest classifier for lymph diseases,” Computer Methods and Programs in Biomedicine, vol. 113, no. 2, pp. 465–473, 2014. View at: Publisher Site | Google Scholar
  33. T. Shaikhina, D. Lowe, S. Daga, D. Briggs, R. Higgins, and N. Khovanova, “Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation,” Biomedical Signal Processing and Control, vol. 37, pp. 1025–1042, 2017. View at: Publisher Site | Google Scholar
  34. S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: a comparative study,” Decision Support Systems, vol. 50, no. 3, pp. 602–613, 2011. View at: Publisher Site | Google Scholar
  35. Y. Tian, X. Ju, Z. Qi, and Y. Shi, “Efficient sparse least squares support vector machines for pattern classification,” Computers & Mathematics with Applications, vol. 66, no. 10, pp. 1935–1947, 2013. View at: Publisher Site | Google Scholar
  36. C. S. Lo and C. M. Wang, “Support vector machine for breast MR image classification,” Computers & Mathematics with Applications, vol. 64, no. 5, pp. 1153–1162, 2012. View at: Publisher Site | Google Scholar
  37. Y. Guo, L. Yu, Z. Wen, and M. Li, “Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences,” Nucleic Acids Research, vol. 36, no. 9, pp. 3025–3030, 2008. View at: Publisher Site | Google Scholar
  38. H. Yang, L. Chan, and I. King, “Support vector machine regression for volatile stock market prediction,” in Intelligent Data Engineering and Automated Learning — IDEAL 2002, vol. 2412, pp. 391–396, 2002. View at: Publisher Site | Google Scholar
  39. C. K. I. Williams, “Learning with kernels: support vector machines, regularization, optimization, and beyond,” Publications of the American Statistical Association, vol. 98, pp. 489–489, 2002. View at: Google Scholar
  40. V. P. Gladis Pushpa Rathi, “A novel approach for feature extraction and selection on MRI images for brain tumor classification,” International Conference on Computer Science, Engineering and Applications., vol. 10, no. 5, pp. 225–234, 2012. View at: Publisher Site | Google Scholar
  41. M. Akhil Jabbar, B. L. Deekshatulu, and P. Chandra, “Classification of heart disease using k-nearest neighbor and genetic algorithm,” Procedia Technology, vol. 10, pp. 85–94, 2013. View at: Publisher Site | Google Scholar
  42. T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967. View at: Publisher Site | Google Scholar
  43. E. A. Aydın and M. K. Keleş, “Breast cancer detection using k-nearest neighbors data mining method obtained from the bow-tie antenna dataset,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 27, no. 6, 2017. View at: Publisher Site | Google Scholar
  44. F. I. Alam, F. K. Bappee, M. R. Rabbani, and M. M. Islam, “An optimized formulation of decision tree classifier,” Communications in Computer and Information Science, vol. 361, pp. 105–118, 2013. View at: Publisher Site | Google Scholar
  45. J. R. Neto, Z. M. de Souza, S. R. de Medeiros Oliveira et al., “Use of the decision tree technique to estimate sugarcane productivity under edaphoclimatic conditions,” Sugar Tech., vol. 19, no. 6, pp. 662–668, 2017. View at: Publisher Site | Google Scholar
  46. K. Boonchuay, K. Sinapiromsaran, and C. Lursinsap, “Decision tree induction based on minority entropy for the class imbalance problem,” Pattern Analysis and Applications, vol. 20, no. 3, pp. 769–782, 2017. View at: Publisher Site | Google Scholar
  47. T. Fawcett, “An introduction to roc analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006. View at: Publisher Site | Google Scholar
  48. Y. U. Chang-Chun, H. E. Jia, S. C. Fan et al., “Application of data mining in medical field,” Academic Journal of Second Military Medical University, vol. 24, pp. 1250–1252, 2003. View at: Google Scholar
  49. D. M. Herrera-Ibatá, A. Pazos, R. A. Orbegozo-Medina, F. J. Romero-Durán, and H. González-Díaz, “Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties,” Bio Systems, vol. 132-133, pp. 20–34, 2015. View at: Publisher Site | Google Scholar
  50. T. A. Almeida, R. M. Silva, and A. Yamakami, “Machine learning methods for spamdexing detection,” International Journal of Information Security Science, vol. 2, pp. 1–22, 2016. View at: Google Scholar
  51. Y. Feng, Z. Wu, R. Detels et al., “HIV/STD prevalence among men who have sex with men in Chengdu, China and associated risk factors for HIV Infection,” Journal of Acquired Immune Deficiency Syndromes, vol. 53, Supplement 1, pp. S74–S80, 2010. View at: Publisher Site | Google Scholar
  52. D. Delen, G. Walker, and A. Kadam, “Predicting breast cancer survivability: a comparison of three data mining methods,” Artificial Intelligence in Medicine, vol. 34, no. 2, pp. 113–127, 2005. View at: Publisher Site | Google Scholar

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