Article of the Year 2021
TXI: Texture and Color Enhancement Imaging for Endoscopic Image EnhancementRead the full article
Journal of Healthcare Engineering provides a vehicle for the exchange of advanced knowledge, emerging technologies, and innovative ideas related to all aspects of engineering involved in healthcare delivery processes and systems.
Chief Editor, Professor Zollo, has research expertise in neuro-robotics and biomedical technologies for neuroscience, rehabilitation and assistance robotics, and robotic and mechatronic devices for personal assistance and service robotics.
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Performance Evaluation of Machine Learning Algorithm for Classification of Unintended Pregnancy among Married Women in Bangladesh
Intended pregnancy is one of the significant indicators of women’s well-being. Globally, 74 million women become pregnant every year without planning. Unintended pregnancies account for 28% of all pregnancies among married women in Bangladesh. This study aimed to investigate the performance of six different machine learning (ML) algorithms applied to predict unintended pregnancies among married women in Bangladesh. From BDHS 2017-18, only 1129 pregnant women aged 15–49 were eligible for this study. An independent test had performed before we considered six popular ML algorithms, such as logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), naïve Bayes (NB), and elastic net regression (ENR) to predict the unintended pregnancy. Accuracy, sensitivity, specificity, Cohen’s Kappa statistic, and area under curve (AUC) value were used as model evaluation. The bivariate analysis result showed that women aged 30–49 years, poor, not educated, and living in male-headed households had a higher percentage of unintended pregnancy. We found various performance parameters for the classification of unintended pregnancy: LR accuracy = 79.29%, LR AUC = 72.12%; RF accuracy = 77.81%, RF AUC = 72.17%; SVM accuracy = 76.92%, SVM AUC = 70.90%; KNN accuracy = 77.22%, KNN AUC = 70.27%; NB accuracy = 78%, NB AUC = 73.06%; and ENR accuracy = 77.51%, ENR AUC = 74.67%. Based on the AUC value, we can conclude that of all the ML algorithms we investigated, the ENR algorithm provides the most accurate classification for predicting unwanted pregnancy among Bangladeshi women. Our findings contribute to a better understanding of how to categorize pregnancy intentions among Bangladeshi women. As a result, the government can initiate an effective campaign to raise contraception awareness.
A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis
The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system.
Comprehensive Diagnostic Medical System Based on Notch1 Signaling Pathway to Inhibit the Growth of Small-Cell Lung Carcinoma
With the gradual application of big data and other technologies to the medical field, more and more people tend to get online medical services. This article mainly studies the comprehensive diagnostic medical system based on Notch1 signaling pathway to inhibit the growth of small-cell lung carcinoma. In the experiment, we used the rapid thawing method to recover the cells and took the logarithmic growth phase cells for cell passage. We calculated the cell concentration and diluted the cells according to the experimental requirements. According to the standard curve, the corresponding sample protein concentration was calculated; at the same time, the Trizol method was used to extract the total RNA, the NanoDrop8000 spectrophotometer was used to determine the RNA concentration, and the RNA quality was detected by agarose gel electrophoresis. We used immunohistochemical staining to complete the staining of lung cancer cells. Finally, black box testing was used to test the functional modules of the system. Experimental data show that the accuracy rate of data obtained by the system reaches 98%, which greatly facilitates doctors and patients. The results show that the system has good ease of use and reliability and improves the diagnosis and treatment of hospital patients.
Guidewire-Assisted Reduction Technology Combined with Postural Reduction Improves the Success Rate of Internal Vein Catheterisation
Objective. To investigate the value of guidewire-assisted reduction technology (which increases the stiffness of a catheter through the use of a guidewire, thereby protecting the puncture point and distal vein from breakage) combined with postural reduction for malpositioned catheters in the internal jugular vein during peripherally inserted central venous catheter catheterisation. Methods. From January 2015 to August 2020, we used ultrasound to perform guided puncture and monitoring. We identified the tip of the catheter as malpositioned in the internal jugular vein in 99 patients during the catheterisation process. These patients were divided randomly into a control group and an experimental group. In the control group, 43 cases received guidewire-assisted reduction technology, while in the experimental group, 56 patients received guidewire-assisted reduction technology combined with an upright posture. This study compared the efficacy of these two methods. Results. The results showed that 30 catheters were reduced successfully in the control group, with a success rate of 69.8%. In the experimental group, 53 cases were successfully reduced, with a success rate of 94.6%. The catheter reduction success rate in the experimental group was significantly higher than in the control group; this was a statistically significant difference (). Conclusion. Guidewire-assisted reduction technology combined with postural reduction can improve the success rate of the reduction of malpositioned catheters in the internal jugular vein.
Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet
Objective. In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161. Methods. The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific method. Then, the Vision Transformer and DenseNet161 models are trained by the fivefold cross-validation method, and the fivefold prediction results corresponding to the two models are fused by different weights. Finally, the five fused results are averaged to obtain the category with the highest probability. Results. The results show that the fusion method in this paper reaches an accuracy rate of 68% for the four classifications of cervical lesions. Conclusions. It is more suitable for clinical environments, effectively reducing the missed detection rate and ensuring the life and health of patients.
Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network
A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. This would allow patients to choose the best accessible kidney donor and the optimal immunosuppressive medication. Several studies have attempted to identify factors that predispose to graft rejection, but the results have been contradictory. As a result, the goal of this paper is to use the African buffalo-based artificial neural network (AB-ANN) approach to uncover predictive risk variables related to kidney graft. These two feature selection approaches combine to provide a novel hybrid feature selection technique that could select the most important elements to improve prediction accuracy. The feature analysis revealed that clinical features have varied effects on transplant survival. The collected data is processed in both training and testing methods. The prediction model's performance, in terms of accuracy, precision, recall, and F-measure, was examined, and the results were compared with those of other existing systems, including naive Bayesian, random forest, and J48 classifier. The results suggest that the proposed approach can forecast graft survival in kidney recipients' next visits in a creative manner and with more accuracy compared with other classifiers. This proposed method is more efficient for predicting kidney graft survival. Incorporating those clinical tools into outpatient clinics’ everyday workflows could help physicians make better and more personalised decisions.