International Journal of Scientific Research and Engineering Development

Cardio vascular diseases (CVDs) are the number one cause of death globally, taking millions of lives every year. Early and accurate diagnosis must be made in order to improve survival rates and save patients seeking timely treatment. This project proposes a machine learning approach to predict the probability of cardiovascular disease from a patient health data. Applications of Logistic Regression, Random Forest, Support Vector Machine (SVM) and Neural Networks were employed to evaluate patients at risk. Trained models were hosted in a web-based application, providing users with an friendly-to-use interface for prediction. This system provides clinicians and individuals with a means of evaluating a patient’s CVD cardiovascular risk, as it can process data without human involvement quickly and provide results in realtime. This project is of the increasing importance in healthcare of using artificial intelligence to provide scalable and accessible diagnostic tools.