Coronary Illness Hazard Prediction using Machine Learning (CIHPML)

Cardio vascular diseases are one of the major causes of death globally.It is very important tofind a precise and well founded approach to automate the accomplished work and thus carrying out effective management. Many researchers used several data mining techniques to understand and help in diagnose heart disease. In order to decrease the deaths from heart disease, you must have a fast and precise detection technique. Early prediction can help people change their lifestyle. It also ensures proper medical treatment if needed. In order to drop down the death rate of heart diseases, a rapid and precise techniquesare needed. The proposed work predicts the possibilities of heart diseases by implementing various number of data mining techniques such as logistic regression, K nearest, decision trees, support vector machine. A web based system is developed in this paper, that can determine whether a person is likely to get affected with heart disease or not based his health factors. It is found that the Support Vector Machine achieved a maximum accuracy of 86.76% against other implemented ML algorithms