A proximal policy optimisation algorithm-based algorithm for cardiovascular disorders detection

Cardiovascular diseases (CVDs) significantly impact athletes, impacting the heart and blood vessels. This article introduces a novel method to assess CVD in athletes through an artificial neural network (ANN). The model utilises the mutual learning-based artificial bee colony (ML-ABC) algorithm to set initial weights and proximal policy optimisation (PPO) to address imbalanced classification. ML-ABC uses mutual learning to enhance the learning process by updating the positions of the food sources with respect to the best fitness outcomes of two randomly selected individuals. PPO makes updates in the ANN stable and efficient to improve the model's reliability. Our approach formulates the classification problem as a series of decision-making processes, rewarding every classification act with higher rewards for correctly identifying the instances of the minority class, hence handling class imbalance. We evaluated the model's performance on a diversified medical dataset including 26,002 athletes who were examined within the Polyclinic for Occupational Health and Sports in Zagreb, further validated with NCAA and NHANES datasets to verify generalisability. Our findings indicate that our model outperforms existing models with accuracies of 0.88, 0.86 and 0.82 for the respective datasets. These results enhance clinical model application and advance cardiovascular disorder detection and methodologies.