Intellectual Coronary Thrombosis Prediction using Naïve Bayes and Decision Trees
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Abstract
The Diagnosis of disease is vital and intricate role in medicine. The recognition of heart disease from diverse feature is a multilayered problem that is not free from false assumptions and is frequently accompanied by impulsive effects. A proficient methodology for the extraction of significant patterns from the heart disease warehouse has been presented .Initially the data warehouse is preprocessed in order to make suitable for the mining process. In this research, the potential list of classification and prediction based data mining techniques has been presented. The conditional probability for having heart disease was estimated by Holdout Confusion method and compared with decision tress. The research work demonstrates better accuracy for Naïve Bayes than decision trees. The result of these evaluations shows the overall performance of naïve Bayes method can be applied successfully for predicting the heart attack effectively. The proposed model is implemented on the C Sharp dot net platform. In future, the work can be further expanded with other data mining techniques such as association rules and integrated with text mining.
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Keywords: Data Mining, Disease Diagnosis, Naïve Bayes, Decision Trees, Holdout Confusion method.
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