Ahmed Alsarori, Ahmed Mohammed and Mohd Herwan, Sulaiman (2025) Integrated deep learning for cardiovascular risk assessment and diagnosis: An evolutionary mating algorithm-enhanced CNN-LSTM. MethodsX, 15 (103466). pp. 1-12. ISSN 2215-0161. (Published)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Cardiovascular disease, Machine learning, Deep learning, Hybrid model, Convolutional neural network, Long Short-Term Memory, Evolutionary mating algorithm |
| Subjects: | R Medicine > RC Internal medicine R Medicine > RD Surgery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical and Electronic Engineering Technology Institute of Postgraduate Studies |
| Depositing User: | Dr. Mohd Herwan Sulaiman |
| Date Deposited: | 09 Jul 2025 00:50 |
| Last Modified: | 09 Jul 2025 00:50 |
| URI: | http://umpir.ump.edu.my/id/eprint/44971 |
| Download Statistic: | View Download Statistics |
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