A Hybrid Heart Disease Prediction System Using Machine Learning Techniques
Nowadays heart disease is one of the life threatening diseases and it causes greater number of deaths in the past few years in all over the world. This disease can be caused by several factors that affect the human heart and can also be caused by genetic factors that can be passed down from generations. In order to provide the preventive treatment in advance, an intelligent diagnosis of heart disease and prevention system is essential. But the diagnosis of heart failure manually through conventional medical records may not be reliable and advisable in many aspects. So a reliable and precise system is indispensable to condense the heart failure and provide preventive measure in advance. Hence, a hybrid prediction system is proposed using machine learning algorithms for the better and accurate diagnosis of the heart disease. The proposed system applies feature selection algorithm and classification algorithms on the heart disease dataset that contains the more suitable attributes and values related for the prediction. The results are validated and accuracy is calculated based on the training and testing dataset.