Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction

• Published in: Scientific reports 2024-12, Vol.14 (1), p.30993-20, Article 30993
• Main Authors: Li, Chengjie, Wang, Yanglin, Meng, Linghui, Zhong, Wen, Zhang, Chengfang, Liu, Tao
• Format: Article
• Language: English
• Subjects: 639/705/117; 692/4019; Algorithms; Cardiovascular disease; Cardiovascular disease (CVD); Cardiovascular diseases; Coronary Artery Disease; Coronary vessels; Elimination strategy; Feature engineering; Feature selection; Health risks; Heart diseases; Hierarchies; Humanities and Social Sciences; Humans; multidisciplinary; Neural networks; Neural Networks, Computer; Particle swarm optimization algorithm; Prediction models; Principal Component Analysis; Principal components analysis; Risk groups; Science; Science (multidisciplinary); Simulated annealing algorithm; Vein & artery diseases
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-82184-2

Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model’s feature learning capability. The proposed method achieved remarkable accuracy rates of 93.22% and 95.20% on the UCI and Kaggle datasets, respectively, underscoring its exceptional performance even with small sample sizes. Ablation experiments further validated the efficacy of the data preprocessing and feature selection techniques employed. Notably, the EPSOSA algorithm surpassed classical optimization algorithms in terms of convergence speed, while also demonstrating improved sensitivity and specificity. This model holds significant potential for facilitating early identification of high-risk patients, which could ultimately save lives and optimize the utilization of medical resources. Despite implementation challenges, including technical integration and data standardization, the algorithm shows promise for use in emergency settings and community health services for regular cardiac risk monitoring.