Enhancing Coronary Artery Disease Prognosis: A Novel Dual-Class Boosted Decision Trees Strategy for Robust Optimization

The rise in stable coronary artery disease (CAD) due to improved survival rates and population growth has increased patient numbers, straining healthcare systems. Machine learning (ML) models are being developed to predict and identify individual risk factors for early treatment, reducing harm to individuals and families. These models can predict hospitalizations, enable close monitoring of high-risk patients, and optimize medical care. Researchers are developing robust models based on ML algorithms and real-world clinical data to aid in early detection, contributing to AI research in healthcare. Advanced ML models analyze medical imaging, genetic markers, lifestyle, and environmental factors to accurately predict coronary heart disease (CHD) start and progression. Our research introduces four novel models based on two-class Logistic Regression (two-class LR), two-class Neural Network (two-class NN), two-class Decision Jungle (two-class DJ), and two-class Boosted DT (two-class BDT). Our comparative analysis reveals that the two-class Boosted DT model is the most effective, achieving an AUC score of 0.991. This model excels in real-time monitoring by predicting minor changes in patient’s health markers, allowing for timely adjustments in treatment plans. It optimizes medication selection, dosing, and intervention timing based on patient characteristics, improving therapeutic efficacy and reducing side effects. The study reveals the transformative potential of these advanced ML models in CAD prediction and management. By focusing on feature selection, algorithm improvement, and integration, our models analyze medical imaging, genetic markers, lifestyle, and environmental factors to accurately predict the onset and progression of CHD. This research proposes valuable insights into the capabilities of these models to revolutionize disease detection and management, ensuring reliable and timely healthcare interventions across various datasets.