Open AccessA survey on data mining techniques used in medicine
Author(s) -
Saba Maleki Birjandi,
Seyed Hossein Khasteh
Publication year - 2021
Publication title -
journal of diabetes and metabolic disorders
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 33
ISSN - 2251-6581
DOI - 10.1007/s40200-021-00884-2
Subject(s) - data mining , decision tree , computer science , naive bayes classifier , association rule learning , cluster analysis , random forest , support vector machine , logistic regression , decision tree learning , data science , machine learning
Data mining is the process of analyzing a massive amount of data to identify meaningful patterns and detect relations, which can lead to future trend prediction and appropriate decision making. Data mining applications are significant in marketing, banking, medicine, etc. In this paper, we present an overview of data mining applications in medicine to provide a clear view of the challenges and previous works in this area for researchers. Data mining techniques such as Decision Tree, Random Forest, K-means Clustering, Support Vector Machine, Logistic Regression, Neural Network, Naive Bayes, and association rule mining are used for diagnosing, prognosis, classifying, constructing predictive models, and analyzing risk factors of various diseases. The main objective of the paper is to analyze and compare different data mining techniques used in the medical applications. We present a summary of the results and provide comparison analysis of the data mining methods employed by the reviewed articles .