Earlier Prediction on the heart disease based on supervised machine learning techniques

Heart disease has been a major life threatening problem in a human being. Due to several reasons such as eating habits, lack of exercise, becoming overweight, and smoking and unhealthy lifestyle habits cause's heart diseases. This motivates us to get a clear idea of the risk factors in our life regarding the cause of heart disease. The proposed method suggests the levels of risk factors according the patients data, so that one can take care of their health properly in order to prevent the heart disease. In this research work, proposed methodology is to segregate heart disease patients based on their risk factors. Firstly applying the feature selection, this is to reduce the records of data and to train the model faster. The five classifiers are support vector machine, K-nearest neighbor, decision tree and random forest are applied. After applying the classifiers a proposed model will segregate the heart disease patients based on their risk factors according to age of the patients. This will be helping for the doctors to analyze the risk factors of the patients.