Analysis of Machine Learning Algorithms for Heart Disease Prediction

In recent times, heart disease prediction is one of the most complicated tasks in medical field. In the modern era, approximately one person dies per minute due to heart disease. Data science plays a crucial role in processing huge amount of data in the field of healthcare. As heart disease prediction is a complex task, there is a need to automate the prediction process to avoid risks associated with it and alert the patient well in advance. This project makes use of heart disease clinical dataset available in UCI machine learning repository. The proposed Work predicts the chances of heart disease and classifies patient’s risk level by implementing different data Mining techniques such as Decision Tree, Logistic Regression, support vector machine, k-Nearest Neighbor, Random Forest and Gradient Boosting. Thus, this paper Presents a comparative study by analyzing the performance of different machine learning algorithms. With the increasing number of deaths due to heart diseases, it has become mandatory to develop a system to Predict heart diseases effectively and accurately. The Aim of the study was to find the most efficient ML Algorithm for detection of heart diseases. This study compares the accuracy score of all above mentioned machine learning algorithms for predicting heart disease using UCI machine learning repository dataset. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm with accuracy score of 85.24% for prediction of heart disease when Compared to all other Classification algorithms used in this analysis which will help to provide better Results and help health professionals in predicting the heart disease effectively and efficiently.