Probable Risk Factors of Heart Disease with Accuracy Performance by Various Machine Learning Approaches

Heart disease is a serious health concern worldwide and it is continuously increasing irrespective of the ages. According to the WHO., 17.9 million deaths are caused by CVD., which is 32% of all global deaths. Precautionary measure is very essential to avoid this escalation. By Machine learning approaches this analysis is predicted to find out the risk factors for heart disease. LR., SVM., GNB., DT., KNN., RF- six classifiers are used in this study to analyze factors behind the disease. Results before data preprocessing and after preprocessing were investigated where the Random forest algorithm has given the highest accuracy. Accuracy of the other four models is in the range of 86-89% with data preprocessing. Precision., recall., f1-score are also improved significantly. By using machine learning a satisfactory prediction of such a critical disease is very much impressive for health care.