Naive Buyers Theorem for Heart Disease Prediction System

The health care industries collect huge amounts of data that contain some hidden information, which is useful for making effective decisions. For providing appropriate results and making effective decisions on data, some advanced data mining techniques are used. In this study, a Heart Disease Prediction System (HDPS) is developed using Naives Bayes and Decision Tree algorithms for predicting the risk level of heart disease. The system uses 15 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for prediction. The HDPS predicts the likelihood of patients getting heart disease. It enables significant knowledge. E. g. Relationships between medical factors related to heart disease and patterns, to be established. We have employed the multi-layer perceptron neural network with back propagation as the training algorithm. The obtained results have illustrated that the designed diagnostic system can effectively predict the risk level of heart diseases. The diagnosis of heart disease in most depends on a complex cmbination of clinical and pathological data. Because of complexity, it exists a significant amount of interest among clinical and professionals and researchers regarding the efficient and accurate prediction of heart dise