Heart-Disease-Analysis

Heart Disease prediction is one of the most complicated tasks in medical field. 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 uses the heart disease data set available on the AWS cloud S3 platform. The project predicts the chances of heart disease and classifies patient's risk level by implementing two data mining techniques they are Decision Tree and Random Forest. Thus, this project analyses the performance of the two machine learning algorithms. The trial results verify that Random Forest algorithm has achieved the highest accuracy compared to Decision Tree.