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Review of Data Mining Techniques Used in Healthcare
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Author(s): Usha Gupta (Manav Rachna International Institute of Research and Studies, India)and Kamlesh Sharma (Manav Rachna International Institute of Research and Studies, India)
Copyright: 2021
Pages: 26
Source title:
Diagnostic Applications of Health Intelligence and Surveillance Systems
Source Author(s)/Editor(s): Divakar Yadav (National Institute of Technology, Hamirpur, India), Abhay Bansal (Amity University, India), Madhulika Bhatia (Amity University, India), Madhurima Hooda (Amity University, India)and Jorge Morato (Universidad Carlos III de Madrid, Spain)
DOI: 10.4018/978-1-7998-6527-8.ch001
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Abstract
Data mining plays a vital role in converting the medical data like text, image, and graphs into meaningful new data, which helps to take the better decision. In this chapter, an overview of the current research is discussed using the data mining techniques for the finding, analysis, and prediction of various diseases. The focus of this study is to identify the well-performing data mining algorithms used on medical and clinical databases. Multiple algorithms have been identified: text-based mining, association rule-based mining, pattern-based mining, keyword-based mining, machine learning, neural network support vector machine, apriori algorithm, k-means clustering, and natural language. Analyses of the algorithm show that there is no single algorithm or model more suitable for diagnosing or predicting diseases. In some scenarios, some algorithms work very well but not in another data set. There are many examples in clinical or medical research where the combination of different algorithms gives good results.
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