Analysis of Naïve Bayes Classification for Diabetes Mellitus
DOI:
https://doi.org/10.26438/ijcse/v6i12.520524Keywords:
Classification, Text Classification, Naïve Bayes, Semantic Analysis, Health CareAbstract
Data Mining plays a major role in the decision making process of any application as in Health Care, Artificial Intelligence, military and weather forecasting. In particular, Classification is used to implement the real time Clinical Decision Support System (CDSS) in health care industry. Thus the CDSS can be viewed as if it predicts the decisions through the supervised learning instances from the training dataset. Here a discrete set of algorithms and techniques are in vogue in the backdrop of classification through supervised learning and hence termed as classification algorithms. Among these classification algorithms, Naïve Bayes is the most familiar which uses the historical data as supervised learning instances. This paper surveys the application of Naïve Bayes classification in health care with specific pertinence to analyzing Diabetic Mellitus disease. It also focuses on the implementation of this specific algorithm in the Diabetic domain to expertise an application.
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