Different Data Mining Approaches Based Medical Text Data

Table 2

The information of analysis methods for medical text data.

Methods	Purpose	Algorithms	Advantages	Shortcomings
Clustering	Classify similar subjects in medical texts	K-means [28, 29]	1.Simple and fast; 2. Scalability and efficiency	1. Large amount of data and time-consuming; 2. More restrictions on use
Classification	Read medical text data for intention recognition	ANN [30, 31]	1. Solve complex mechanisms in text data; 2. High degree of self-learning; 3. Strong fault tolerance	1. Slow training; 2. Many parameters and difficulty in adjusting parameters
		Decision tree [32, 33]	1. Handle continuous variables and missing values; 2. Judge the importance of features	1. Overfitting; 2. The result is unstable
		Naive bayes [34]	1. The learning process is easy; 2. Good classification performance	Higher requirements for data independence
Association rules	Mine frequent items and corresponding association rules from massive medical text datasets	Apriori [35, 36]	Simple and easy to implement	Low efficiency and time-consuming
		FP-tree [37]	1. Reduce the number of database scans; 2. Reduce the amount of memory space	High memory overhead
		FP-growth [38]	1. Improve data density structure; 2. Avoid repeated scanning	Harder to achieve
Logistic Regression	Analyze how variables affect results	Logistic regression [39]	1.Visual understanding and interpretation; 2. Very sensitive to outliers	1.Easy underfitting; 2. Cannot handle a large number of multiclass features or variables