Comparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection

Citations Over TimeTop 1% of 2025 papers

Abstract

BESO significantly enhances feature selection, with RF emerging as the optimal classifier (92 % accuracy) and substantially outperforming established clinical risk scores. This study highlights the potential of AI-driven CAD diagnosis, supporting early detection and improved patient outcomes. Future work should focus on prospective validation and clinical implementation.

Related Papers

• → On the effectiveness of multilevel selection(2016)4 cited
• → Avoidance of Feature Interactions at Run-Time(2006)3 cited
• → Methods of Artificial Selection(2022)1 cited
• → Selection and correlated responses to selection.(2011)
• → Promising biomarkers in patients with advanced coronary artery disease(2020)