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Abstract There is huge amount of information accessible within the healthcare systems. But there do not exist enough analysis tools to mine uncovered, unusual but useful patterns in data. Data mining has been used successfully in various fields to discover hidden patterns and trends, alerting about the hidden anomalies in the data or simply helping in the decision making process. This paper how classification techniques in data mining can be applied for heart disease prediction. To predict and alert about any future coronary ailment in the patients techniques like Naïve Bayes, and Decision tree are applied and efficiency of these algorithms is compared. The dataset taken is Cleveland dataset with 14 attributes.
Fadnavis et al. (Sat,) studied this question.