AI-Based Prediction of Cardiovascular Disease

Cardiovascular diseases (CVD) are still one of the leading causes of death in the world today which shows that early diagnosis and preventive measures are vital for improving the outcomes post death. The purpose of this project is to determine the potential risk of a patient suffering from cardiovascular disease through the application of artificial intelligence (AI), deep learning (DL) and machine learning (ML). The health parameters include age, gender, blood pressure, cholesterol levels, heart rate, exercise tolerance and the dataset also has a target variable characteristic responsible for indicating the existence or nonexistence of cardiovascular diseases. We aim to construct models that estimate the risk levels of CVD for the patients and use various classification techniques including Support Vector Machines, Random Forests, Decision Trees and Logistic Regression. The performance of the models is evaluated through classification, confusion matrix measures, and accuracy checks.