Enhancing the Detection of Coronary Artery Disease Using Machine Learning

TL;DR

This paper demonstrates that machine learning models, especially a hybrid Bi-LSTM+GRU, significantly improve the accuracy of coronary artery disease detection over traditional methods, supporting personalized healthcare.

Contribution

The study introduces a hybrid Bi-LSTM+GRU model trained on diverse patient data, achieving high accuracy and setting a new benchmark for ML-based CAD diagnosis.

Findings

Hybrid model achieved 97.07% accuracy

ML models outperformed traditional diagnostic methods

Advanced preprocessing enhanced model performance

Abstract

Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. Early detection is critical to recover patient outcomes and decrease healthcare costs. In recent years, machine learning (ML) advancements have shown significant potential in enhancing the accuracy of CAD diagnosis. This study investigates the application of ML algorithms to improve the detection of CAD by analyzing patient data, including clinical features, imaging, and biomarker profiles. Bi-directional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and a hybrid of Bi-LSTM+GRU were trained on large datasets to predict the presence of CAD. Results demonstrated that these ML models outperformed traditional diagnostic methods in sensitivity and specificity, offering a robust tool for clinicians to make more informed decisions. The experimental results show that the hybrid model achieved an accuracy of 97.07%. By integrating advanced data preprocessing techniques and feature selection, this study ensures optimal learning and model performance, setting a benchmark for the application of ML in CAD diagnosis. The integration of ML into CAD detection presents a promising avenue for personalized healthcare and could play a pivotal role in the future of cardiovascular disease management.