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All Journal Jurnal Pendidikan Teknologi dan Kejuruan MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminas Competitive Advantage Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Journal of Development Research JOIV : International Journal on Informatics Visualization Applied Information System and Management IJISTECH (International Journal Of Information System & Technology) Systemic: Information System and Informatics Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems IJISTECH Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Janitra Informatika dan Sistem Informasi Bulletin of Social Informatics Theory and Application Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Yunifa Miftachul Arif
Department Of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Journal of Development Research

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Employing Multi-Layer Perceptron Models for Heart Failure Disease Prediction Hamid, Abdulhalim Hamid Salih; Arif, Yunifa Miftachul; Hariyadi, M. Amin
Journal of Development Research Vol. 9 No. 1 (2025): Volume 9, Number 1, May 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v9i1.430

Abstract

This study aims to develop a predictive model for heart failure using a multilayer perceptron (MLP) as part of the application of deep learning techniques in medical data analysis. Given the increasing prevalence of heart failure and its significant impact on patients' quality of life and healthcare costs, early detection is of paramount importance. The dataset, obtained from Kaggle, consists of 918 medical records containing 12 key health variables, including age, blood pressure, cholesterol level, and fasting blood sugar. The model underwent extensive training and testing, and its performance was evaluated using statistical measures such as precision, recall, accuracy, and AUC-ROC curve. The results showed that the proposed model achieved a prediction accuracy of 91.1%, with a sensitivity of 90.3% and a specificity of 92%, indicating its effectiveness in predicting heart failure compared to traditional models. Further analysis identified ST-segment depression, resting blood pressure, and cholesterol level as the most influential factors in determining the risk of heart failure. Based on these results, the MLP model can be considered an effective tool to assist physicians in the early diagnosis of heart failure. Optimization techniques such as particle swarm optimization (PSO) can be used to improve prediction accuracy. Furthermore, combining the model with advanced analytical methods may enhance its predictive performance. This study highlights the importance of using artificial neural networks in the medical field, emphasizing their role in improving early diagnosis systems, reducing heart failure complications, and improving the overall quality of healthcare services.
Co-Authors AA Sudharmawan, AA Achmad Sabar, Achmad Ady Wicaksono Agariadne Dwinggo Samala Alfachruddin, M. Nabil Fahd Aristantia, Yuliana Bojic, Ljubisa Cahyo Crysdian Coelho, Diogo Pereira Dedy Kurnia Setiawan Duvan Deswantara Putra Dyah Wardani Fachrul Kurniawan Fathir Fathir Fathir Fathurrahman Firmansyah, Rezky Fresy Nugroho Hamid, Abdulhalim Hamid Salih Hani Nurhayati Howard, Natalie-Jane Ihsan, Afif Nuril Ikhlayel, Mohammed Imamudin, M. Janitra, Geovanni Azam Juniardi Nur Fadila Linda Salma Angreani Mauludiah, Siska Farizah Mauridhi H Purnomo Mochamad Hariadi Mochammad Wahyu Firmansyah Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faisal Muhammad Sahi Nauman Khan Norizan Mat Diah Putra, Dony Darmawan Rawas, Soha Ririen Kusumawati Rizqi Aulia Nadhifah Rony, Zahara Tussoleha Roro Inda Melani Safitri A Basid, Puspa Miladin Nuraida Setiyawan, Niko Heri Sulika Sulika Supeno Mardi S. N, Supeno Mardi Supeno Mardi Susiki Nugroho, Supeno Mardi Supriyono Tsoy, Dana Wahyuliningtyas, Lia Zainal Abidin Zulfiandri Zulfiandri