A Clinical Support System for Prediction of Heart Diseases using Machine Learning Techniques

Abstract

In recent times, heart disease prediction is one of the complicated and major cause of death throughout the world. In this era, approximately one person dies per minute due to heart disease. This cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. An automated system in medical diagnosis would enhance medical efficiency and also reduce costs. We will design a system that can efficiently discover the rules to predict the risk level of patients based on the given parameters about their health. The goal is to extract hidden patterns by applying data mining techniques such as Convolutional neural network (CNN), Support Vector machine(SVM), Decision Tree and Random forest which are noteworthy to heart diseases and to predict the presence of heart disease in patients where the presence is valued on a scale. Predicting heart disease requires massive amounts of data that are too complex to process and analyse by conventional methods. Our goal is to find machine learning techniques that are computationally efficient and accurate in predicting heart disease. Data mining combines statistical analysis machine learning and database techniques to extract hidden patterns and relationships from large databases.