Detection of Cardiovascular Disease using Machine Learning Classification Models
Hana H. Alalawi, Manal S. Alsuwat
Abstract
Cardiovascular disease (CVD) is an all-encompassing term for situations affecting the heart or blood vessels. This is commonly associated with an accumulation of fatty deposits within the arteries (atherosclerosis) and an increased risk of developing blood clots. Cardiovascular disease is considered one of the largest causes of morbidity and mortality in the world's population. Predicting and diagnosing the disease is a critical challenge in clinical data analysis and health care providers to prevent people from contracting such a disease and conserve lives. Healthcare industries collect massive amounts of data that contain some information related to heart disease diagnosis, which is serviceable in making effective decisions. Furthermore, AI algorithms and deep neural networks can be used to analyze and diagnose heart disease. The project intends to automatically detect cardiovascular disease using two datasets through a deep learning network and a variety of machine learning classification models. The performance evaluated based on the accuracy, precision, recall, and f-score for each of the models. Hence, the Random Forest model achieved the highest performance at 94% accuracy in the heart diseases dataset, while Gradient Boosting model achieved the highest performance at 73% accuracy, 73% Recall, 73% F1-score, and 74% Precision in Cardiovascular Disease Dataset.