Machine learning algorithms for predicting coronary artery disease: efforts toward an open source solution

• Published in: Future science OA Vol. 7; no. 6; p. FSO698
• Main Authors: Akella, Aravind, Akella, Sudheer
• Format: Journal Article
• Language: English
• Published: England Future Science Ltd 01.07.2021; Taylor & Francis Group
• Subjects: CAD; heart dataset; heart disease; ML algorithms; neural networks; heart dataset; heart disease; CAD; ML algorithms; neural networks
• ISSN: 2056-5623; 2056-5623
• DOI: 10.2144/fsoa-2020-0206

The development of coronary artery disease (CAD), a highly prevalent disease worldwide, is influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist clinicians in timely detection of CAD and may improve outcomes. In this study, we applied six different ML algorithms to predict the presence of CAD amongst patients listed in ‘the Cleveland dataset.’ The generated computer code is provided as a working open source solution with the ultimate goal to achieve a viable clinical tool for CAD detection. All six ML algorithms achieved accuracies greater than 80%, with the ‘neural network’ algorithm achieving accuracy greater than 93%. The recall achieved with the ‘neural network’ model is also the highest of the six models (0.93), indicating that predictive ML models may provide diagnostic value in CAD. Coronary artery disease (CAD) is correlated with many preventable risk factors. Early diagnosis of CAD allows for prevention of worsening of CAD and its complications. This study aims to utilize machine learning (ML) algorithms to predict for CAD in patients. Our results indicate that ML algorithms can accurately predict for CAD. Furthermore, by providing our code publicly, we hope to improve the ability for ML algorithms as a diagnostic tool for CAD.