Diagnosis of Coronary Artery Disease using Adult Data from Blood Tests and Electrocardiograms

Abstract

Objective:
 Many modifiable risk factors affect the onset of coronary artery disease (CAD), a condition that is extremely common throughout the globe. Predictive models created using machine learning (ML) algorithms may help physicians identify CAD earlier and may lead to better results. The goal of this project was to use ML algorithms to predict CAD in patients.
 
 Methods:
 The gathered dataset of UCI heart disease was used in this study to evaluate a variety of machine learning methods to predict CAD. Just the most crucial aspects of the hypothesis testing method were kept. Support vector machines (SVM) were used in a comparative analysis employing a variety of assessment measures.
 
 Results:
 All machine learning methods achieved accuracy levels of at least 80%, with the SVM algorithm obtaining accuracy levels of at least 90%. Predictive ML models had high diagnostic relevance in CAD, as seen by the SVM model's high recall (0.9), which is was the highest of all the models.
 
 Conclusion:
 The findings of the current study demonstrated that, independent of the measures used to evaluate machine learning models, feature selection has a significant impact on performance. Finding the most useful features is thus crucial. SVM was chosen as the top model based on the features we considered.