Utilizing evolutionary mating algorithm optimized deep learning to assess cardiovascular diseases risk

Cardiovascular Diseases (CVD) continue to be a primary cause of death worldwide, underscoring the critical importance of early and accurate risk prediction. However, traditional predictive models struggle with the complexity and interdependencies in medical data. This study addresses this gap by proposing a deep learning-based risk assessment model optimized with the Evolutionary Mating Algorithm (EMA) to enhance prediction accuracy and efficiency. Our contributions include developing a dedicated risk variable for machine learning applications and benchmarking the EMA-optimized model against ADAM and Particle Swarm Optimization (PSO). The proposed method was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R²), and Standard Deviation (STD). Experimental results demonstrate that the EMA-optimized model outperforms traditional optimization methods, achieving an MAE of 0.037, RMSE of 0.0464, and an R² of approximately 0.91. These results highlight the effectiveness of EMA in enhancing cardiovascular risk assessment models, providing a more reliable tool for early diagnosis and clinical decision-making.