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A Comprehensive Survey of Data Mining Techniques in Disease Prediction

A Comprehensive Survey of Data Mining Techniques in Disease Prediction

Durgadevi Mullaivanan, Kalpana R.
Copyright: © 2021 |Pages: 27
ISBN13: 9781799825661|ISBN10: 1799825663|ISBN13 Softcover: 9781799825678|EISBN13: 9781799825685
DOI: 10.4018/978-1-7998-2566-1.ch002
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MLA

Mullaivanan, Durgadevi, and Kalpana R. "A Comprehensive Survey of Data Mining Techniques in Disease Prediction." Challenges and Applications of Data Analytics in Social Perspectives, edited by V. Sathiyamoorthi and Atilla Elci, IGI Global, 2021, pp. 27-53. https://doi.org/10.4018/978-1-7998-2566-1.ch002

APA

Mullaivanan, D. & R., K. (2021). A Comprehensive Survey of Data Mining Techniques in Disease Prediction. In V. Sathiyamoorthi & A. Elci (Eds.), Challenges and Applications of Data Analytics in Social Perspectives (pp. 27-53). IGI Global. https://doi.org/10.4018/978-1-7998-2566-1.ch002

Chicago

Mullaivanan, Durgadevi, and Kalpana R. "A Comprehensive Survey of Data Mining Techniques in Disease Prediction." In Challenges and Applications of Data Analytics in Social Perspectives, edited by V. Sathiyamoorthi and Atilla Elci, 27-53. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2566-1.ch002

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Abstract

In recent days, data mining has become very popular, and numerous research works have been carried out of using data mining techniques in the healthcare sector. The healthcare transactions generate a massive amount of data which are very voluminous and complex to be processed. Therefore, data mining techniques have been employed, which provides a practical methodology for transforming the massive amount of data into efficient knowledge for the process of decision making. Prediction and classification are the two forms of data analysis methods. However, it is still difficult to explore the complete literature in the healthcare domain. This chapter reviews the research overview that is done in the healthcare sector utilizing different data mining methodologies for prediction and classification of diverse diseases. Also, a detailed comparison of reviewed methods takes place for better understanding of the existing models. An extensive experimental study is also performed to analyze the performance of data mining algorithms.

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