Supplementary material.docx: Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution

Supplementary Material. Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution

1. R file containing the computer code generated in this study

2. Additional analysis of the dataset (Figures)

3. Supplementary list of recently published articles that encompass ML algorithms with detection and diagnosis of disease

Abstract:

Introduction

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.

Materials and Methods

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.

Results

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.