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Overview of Predictive Modeling Approaches in Health Care Data Mining

Overview of Predictive Modeling Approaches in Health Care Data Mining

Sunita Soni
Copyright: © 2016 |Pages: 23
ISBN13: 9781466695627|ISBN10: 1466695625|EISBN13: 9781466695634
DOI: 10.4018/978-1-4666-9562-7.ch004
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MLA

Soni, Sunita. "Overview of Predictive Modeling Approaches in Health Care Data Mining." Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 73-95. https://doi.org/10.4018/978-1-4666-9562-7.ch004

APA

Soni, S. (2016). Overview of Predictive Modeling Approaches in Health Care Data Mining. In I. Management Association (Ed.), Business Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 73-95). IGI Global. https://doi.org/10.4018/978-1-4666-9562-7.ch004

Chicago

Soni, Sunita. "Overview of Predictive Modeling Approaches in Health Care Data Mining." In Business Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 73-95. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9562-7.ch004

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Abstract

Medical data mining has great potential for exploring the hidden pattern in the data sets of the medical domain. A predictive modeling approach of Data Mining has been systematically applied for the prognosis, diagnosis, and planning for treatment of chronic disease. For example, a classification system can assist the physician to predict if the patient is likely to have a certain disease, or by considering the output of the classification model, the physician can make a better decision on the treatment to be applied to the patient. Once the model is evaluated and verified, it may be embedded within clinical information systems. The objective of this chapter is to extensively study the various predictive data mining methods to evaluate their usage in terms of accuracy, computational time, comprehensibility of the results, ease of use of the algorithm, and advantages and disadvantages to relatively naive medical users. The research has shown that there is not a single best prediction tool, but instead, the best performing algorithm will depend on the features of the dataset to be analyzed.

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