Journal
INTERNATIONAL CARDIOVASCULAR RESEARCH JOURNAL
Volume 16, Issue 2, Pages 72-79Publisher
SHIRAZ UNIV MEDICAL SCIENCES, CARDIOVASCULAR RESEARCH CENTER
Keywords
Machine Learning, Angiography; Random Forest; Artificial Neural Network; Coronary Artery Disease; Risk Factor
Categories
Funding
- Mashhad University of Medical Sciences [981532]
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This study compared the application of Random Forest (RF) and Artificial Neural Network (ANN) in diagnosing Coronary Artery Disease (CAD). The results showed that both methods could predict the outcome of coronary angiography by considering multiple features, and identified several important predictive factors.
Background: Coronary Artery Disease (CAD) is the most common type of cardiovascular disorders. Despite being costly and invasive, coronary angiography is a reliable method for diagnosing CAD. Therefore, it is crucial to use non-invasive methods to screen candidates for angiography to accelerate the process of decision-making. Two powerful Machine Learning (ML) methods are Random Forest (RF) and Artificial Neural Network (ANN). Objectives: The present study aimed to compare RF and ANN to define the most important features for positive CAD results and predict the need for angiography as a screening method. Methods: This cross-sectional study was performed on 1128 patients referred for angiography. The data were divided into test and train sets. The models (RF and ANN) were fitted with the angiographic outcome variable (positive or negative) as the dependent variable and five features as predictors. Then, the performances of the models were compared by considering the Area Under the Rock Curve (AUC). All statistical analyses were done using the R software, version 4.1.2. Results: Out of the 1128 patients, 752 (66.7%) had positive angiography results. The AUC values were 0.75 and 0.52 for the test data set in ANN and RF models, respectively. Conclusion: Fasting Blood Sugar (FBS), gender, age, Body Mass Index (BMI), and smoking habit were important in predicting the results of an angiography for CAD. Applying these factors in ML approaches can be considered a screen for angiography to accelerate the process of diagnosis.
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