MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

• Published in: Physiological measurement Vol. 44; no. 12; pp. 125012 - 125025
• Main Authors: Bostani, Arsam, Mirzaeibonehkhater, Marzieh, Najafi, Hamidreza, Mehrtash, Mohammad, Alizadehsani, Roohallah, Tan, Ru-San, Acharya, U Rajendra
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.12.2023
• Subjects: cardiovascular disease; ECG; imbalanced classification; multilayer perceptron; reinforcement learning; ECG; Cardiovascular disease; Multilayer perceptron; Imbalanced classification; Reinforcement learning
• DOI: 10.1088/1361-6579/ad1459
• ISSN: 0967-3334

Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26,002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed. The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals "at risk" and "not at risk," respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4\%; geometric mean 89.6\%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model. This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.