Machine Learning, heart disease prediction, feature selection, prediction model,Classifications algorithms, cardiovascular disease(CVD).

In HRFLM, I use a computational approach with the three association rules of mining namely, apriority, predictive and Tertius to find the factors of heart disease on the UCI Cleveland dataset. The available information points to the deduction that females have less of a chance for heart disease compared to males. In heart diseases, accurate diagnosis is primary. But, the traditional approaches are inadequate for accurate prediction and diagnosis. HRFLM makes use of ANN with back propagation along with 13 clinical features as the input. The obtained

results are comparatively analyzed against traditional methods. The risk levels become very high and a number of attributes are used for accuracy in the diagnosis of the disease. The nature and complexity of heart disease require an efficacious treatment plan. Data mining methods help in remedial situations in the medical field. The data mining methods are further used considering DT, NN, SVM, and KNN. Among several employed methods, the results from SVM prove to be useful in enhancing accuracy in the prediction of disease. The nonlinear method with a module for monitoring heart function is introduced to detect the arrhythmias like bradycardia, tachycardia, atrial, atrial ventricular utters, and many others. The performance efficacy of this method can be estimated from the accuracy in the outcome results based on ECG data. ANN training is used for the accurate diagnosis of disease and the prediction of possible abnormalities in the patient.

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