Efficient Heart Disease Prediction with Suggestion using Feature Selection and IE

By current technology development, detection of disease can be done through analysis of datasets through data mining. More number of diseases has been accurately identified through mining process and in advance technique such as machine learning. In our propose heart disease has been detected through mining in accurate way. There is wealth of data available within the health care system. However, there is lack of effective tools to discover hidden relationships and trends in data. Advanced data mining techniques can help remedial situations. In existing system a prototype using data mining techniques mainly Naïve Bayes and WAC (Weighted Associated Classifier) has been utilized. In our proposed Support Vector Machine (SVM) and information extraction algorithm is utilized for efficient detection of heart disease and intimating current condition of patient through machine learning technique. The dataset is composed of important factors such as age, sex, diabetic, height, weight, blood pressure, cholesterol, fasting blood sugar, hypertension, other disease. Hence this system indicates whether patient had a risk of heart disease or not with suggestion in efficient way.