Utilizing Evolutionary Mating Algorithm Optimized Deep Learning to Assess Cardiovascular Diseases Risk

DOI: https://doi.org/10.24003/emitter.v13i1.936
Keywords: Heart Disease, Deep learning, Evolutionary mating algorithm, Artificial Neural Network, Risk prediction

Abstract

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.

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Author Biographies

Ahmed Alsarori, Universiti Malaysia Pahang Al-sultan Abdullah

Ahmed Mohammed Ahmed Alsarori obtained his B.eng in Mechatronics Engineering (Hons) and Msc in Data Science and Business Analytics from Asia Pacific University (APU) and De Montfort University (DMU) in 2019 and 2021 respectively. Currently pursuing a PhD as a research assistant and student in Unversiti Malaysia Pahang Al-Sultan Abdullah (UMPSA).

Mohd Herwan Sulaiman, Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA)

Mohd Herwan Sulaiman obtained his B. Eng. (Hons) in Electrical-Electronics, M. Eng (Electrical-Power) and PhD (Electrical Engineering) from Universiti Teknologi Malaysia (UTM) in 2002, 2007 and 2012 respectively. He currently serves as an Associate Professor at Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA). His research interests are power system optimization and swarm intelligence applications to power system studies. He has authored and co-authored more than 150 technical papers in the international journals and conferences and has been invited as a Journal reviewer for several international impact journals in the field of power system, soft computing application and many more. He is also a Senior Member of IEEE.

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Published
2025-06-17
How to Cite
Alsarori, A., & Sulaiman, M. H. (2025). Utilizing Evolutionary Mating Algorithm Optimized Deep Learning to Assess Cardiovascular Diseases Risk. EMITTER International Journal of Engineering Technology, 13(1), 124-138. https://doi.org/10.24003/emitter.v13i1.936
Issue
Vol 13 No 1 (2025)
Section
Articles

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