Machine Learning-Based Prediction of Coronary Artery Disease Using Clinical and Behavioral Data: A Comparative Study
Abdulkadir Çakmak1, Gülşah Akyilmaz2, Aybike Gizem Köse3
1Department of Cardiology, Faculty of Medicine, Amasya University, Amasya 05200, Türkiye.
Insights
Machine learning models accurately detect coronary artery disease (CAD) by integrating clinical and behavioral data. The k-nearest neighbors (k-NN) model showed the highest accuracy, highlighting potential for improved early diagnosis.
Area of Science:
- Cardiology and Artificial Intelligence
- Biomedical Data Science
- Predictive Analytics in Healthcare
Background:
- Coronary artery disease (CAD) is a major global health concern requiring accurate early diagnosis.
- Machine learning (ML) offers advanced tools for integrating complex patient data to improve diagnostic capabilities.
- Multidimensional patient data integration is key for enhancing risk stratification and clinical management of CAD.
Purpose of the Study:
- To develop and compare supervised ML algorithms for early CAD diagnosis.
- To evaluate the efficacy of demographic, anthropometric, biochemical, and psychosocial parameters in CAD prediction.
- To identify the most effective ML model for distinguishing CAD patients from controls.
Main Methods:
- Retrospective analysis of 300 adult patients (165 with CAD, 135 controls).
- Utilized a dataset including 21 biochemical markers, body composition, and eating behavior scores.
- Trained and evaluated six ML algorithms (k-NN, SVM, ANN, LR, NB, DT) using 10-fold cross-validation.
Main Results:
- The k-NN model achieved the highest accuracy (98.33%) and AUC (0.99), followed by SVM and ANN.
- CAD patients showed elevated glucose, triglycerides, LDL cholesterol, and abdominal obesity, with lower vitamin B12.
- Psychosocial factors like emotional eating had limited impact on model performance.
Conclusions:
- Supervised ML models, especially k-NN, SVM, and ANN, demonstrate high accuracy in CAD detection.
- Integrating diverse clinical and behavioral data enhances CAD prediction beyond traditional biomarkers.
- This approach shows promise for improving early CAD diagnosis and patient risk stratification.
Abstract:
Background and Objectives: Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. An early and accurate diagnosis is essential for effective clinical management and risk stratification. Recent advances in machine learning (ML) have provided opportunities to enhance the diagnostic performance by integrating multidimensional patient data. This study aimed to develop and compare several supervised ML algorithms for early CAD diagnosis using demographic, anthropometric, biochemical, and psychosocial parameters. Materials and Methods: A total of 300 adult patients (165 CAD-positive and 135 controls) were retrospectively analyzed using a dataset comprising 21 biochemical markers, body composition metrics, and self-reported eating behavior scores. Six ML algorithms, k-nearest neighbors (k-NNs), support vector machines (SVMs), artificial neural networks (ANNs), logistic regression (LR), naïve Bayes (NB), and decision trees (DTs), were trained and evaluated using 10-fold cross-validation. Model performance was assessed based on accuracy, sensitivity, false-negative rate, and area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: The k-NN model achieved the highest performance, with 98.33% accuracy and an AUC of 0.99, followed by SVM (96.67%, AUC = 0.95) and ANN (95.33%, AUC = 0.98). Patients with CAD exhibited significantly higher levels of glucose, triglycerides (TGs), LDL cholesterol (LDL-C), and abdominal obesity, while vitamin B12 levels were lower (p < 0.001). Although emotional and mindful eating scores differed significantly between the groups, their contribution to model performance was limited. Conclusions: Machine learning models, particularly k-NN, SVM, and ANN, have demonstrated high accuracy in distinguishing CAD patients from healthy controls when applied to a diverse set of clinical and behavioral variables. This study highlights the potential of integrating psychosocial and clinical data to enhance CAD prediction models beyond traditional biomarkers.
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