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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Elastisitas Jurnal Ekonomi Pembangunan Patisambhida : Jurnal Pemikiran Buddha dan Filsafat Agama
Heru Supriyanto
Prodi Statistika Jurusan Matematika FMIPA Universitas Brawijaya
Religion Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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Journal : Sinkron : Jurnal dan Penelitian Teknik Informatika

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MLP Model Optimization for Heart Attack Risk Prediction: A Systematic Literature Review Supriyanto, Heru; Hariguna, Taqwa; Barkah, Azhari Shouni
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.15027

Abstract

Heart disease remains a leading cause of global mortality, making the development of accurate predictive models a clinical priority. While Multilayer Perceptron (MLP) models offer significant potential, their application is hindered by challenges in optimization, data imbalance, and interpretability. This systematic literature review aims to address these issues by synthesizing current research on MLP model optimization for heart disease prediction, focusing on strategies for handling class imbalance and achieving model transparency with SHapley Additive exPlanations (SHAP). Following PRISMA guidelines, a structured search of major scientific databases resulted in the in-depth analysis of 30 peer-reviewed studies. The findings indicate that MLP optimization is increasingly sophisticated, employing automated hyperparameter tuning and novel architectures. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is the predominant data-level solution, though a trend towards advanced algorithm-level techniques is emerging. The application of SHAP has successfully validated models by confirming the importance of known clinical risk factors like age and chest pain type, while also demonstrating potential for new discovery. This review concludes by providing a comprehensive roadmap for researchers, highlighting a critical need for comparative studies on imbalance techniques, deeper applications of explainable AI for local-level analysis, and a stronger focus on validation using large-scale, real-world clinical data to develop truly robust and trustworthy predictive systems.
Co-Authors Azhari Shouni Barkah Sholawatunnisa, Dinda Pusparahmi Taqwa Hariguna