Classification models on cardiovascular disease detection using Neural Networks, Naïve Bayes and J48 Data Mining Techniques

Mudasir M Kirmani, Syed Immamul Ansarullah


The huge amounts of data generated by healthcare transactions are complex and voluminous which needs to be processed and analyzed by different traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. In today’s modern world cardiovascular disease is the most lethal one. Diagnosis of heart disease is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. This research paper investigates three different classification models of Data Mining Techniques for detection of cardiovascular disease to facilitate experts in the healthcare domain. This research paper highlights the performance of all the three classifications models on cardiovascular disease detection and the same has been justified with the results of different experiments conducted using WEKA machine learning software.

Key words: Data mining, Decision Tree, Multilayer Perception, Naive Bayes, cardiovascular disease, Coronary heart disease

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