Heart failure prediction

Heart Disease prediction is one of the most complicated tasks in the medical field. In the modern era, approximately one person dies per minute due to heart disease. Data science plays a crucial role in processing a 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 paper makes use of the heart disease 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 and Random Forest. Thus, this paper presents a comparative study by analyzing the performance of different machine learning algorithms. The trial results verify that the Decision Tree algorithm has achieved the highest accuracy of 95% compared to other ML algorithms implemented.