Integrated deep learning for cardiovascular risk assessment and diagnosis: An evolutionary mating algorithm-enhanced CNN-LSTM

• Published in: MethodsX Vol. 15; p. 103466
• Main Authors: Ahmed Alsarori, Ahmed Mohammed, Sulaiman, Mohd Herwan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2025; Elsevier
• Subjects: Cardiovascular disease; Computer Science; Convolutional neural network; Deep learning; Dual Output CNN-LSTM-EMA; Evolutionary mating algorithm; Hybrid model; Long Short-Term Memory; Machine learning; Deep learning; Dual Output CNN-LSTM-EMA; Evolutionary mating algorithm; Convolutional neural network; Machine learning; Cardiovascular disease; Hybrid model; Long Short-Term Memory
• ISSN: 2215-0161; 2215-0161
• DOI: 10.1016/j.mex.2025.103466

Cardiovascular diseases (CVD) remain the leading cause of mortality worldwide, emphasizing the urgent need for accurate and efficient predictive models. This study proposes a dual-output deep learning model based on a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, optimized using the Evolutionary Mating Algorithm (EMA). The model predicts both a continuous risk score and a binary diagnostic outcome, supporting both quantitative assessment and early clinical decision-making. EMA was applied for hyperparameter optimization, demonstrating improved convergence and generalization over conventional methods. Performance was benchmarked against CNN-LSTM models optimized using Particle Swarm Optimization (PSO) and Barnacle Mating Optimization (BMO). The EMA-based model achieved superior results, with a Mean Absolute Error (MAE) of 0.018, Mean Squared Error (MSE) of 0.0006, Root Mean Squared Error (RMSE) of 0.024, and a coefficient of determination (R²) of 0.98 for risk prediction. For the diagnostic task, the model attained 70 % accuracy and 80 % precision. These findings validate EMA’s effectiveness in tuning dual-output deep learning models and highlight its potential in enhancing cardiovascular risk stratification and early diagnosis in clinical settings.•Dual-output CNN-LSTM model optimized using EMA.•Continuous risk scores and binary diagnostic classification predictions.•EMA outperformed PSO and BMO in predictive accuracy and model robustness. [Display omitted]