Prediction of Coronary Heart Disease using Deep Learning Models

Abstract

Abstract: More over 80% of deaths from heart disease, including those in Nigeria, are caused by coronary artery disease (CAD), the most prevalent type. The victims were mostly less than 70 years old. More than 17 million people died in 2015 from CVDrelated causes, accounting for more than 30% of all deaths worldwide. It develops over time and progresses through several stages. The stages of CAD include Fatty Streaks, Mild atherosclerosis, Moderate atherosclerosis, Sever atherosclerosis. In this paper, a diagnostic CAD dataset that was collected from the Kaggle website was used to construct a machine learning predictive model for CAD. The dataset was used to create prediction models using machine learning techniques like Naive Bayes, Support Vector Machine, Random Forest, and Gradient Boosting and DNN are applied to compare the results and analysis of the CAD Disease. The models' accuracy, precision, recall, and f1score scores were assessed using performance evaluation methodologies in order to determine the CAD that would have or had already occurred based on a person’s physical condition and data from medical records. Result shows that compared to ML algothims and DL technique, DNN gives more accuracy in less time for the prediction. The prediction accuracy obtained by DNN algorithm is 93.56% and the prediction accuracy obtained by SVM is 83.34%.