Classification and Prediction on Cardiovascular disease datasets

Abstract

Cardiovascular disease is the leading cause of death worldwide and in the U.S. Almost half of adults in the U.S. have some form of cardiovascular disease. It affects people of all ages, sexes, ethnicities and socioeconomic levels. However, people who have Cardiovascular diseases might be asymptomatic, which means the patient does not feeling anything at all. Asymptomatic patients would not get diagnosed until they reach a more serious stage and may miss the best time for treatment. The aim of this project is to collect data on cardiovascular disease, analyze the data and use them to build a predictive machine learning model for early-stage heart disease detection. Multiple different data pre-processing and classification methods have been applied and compared for the best prediction accuracy.