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Yunifa Miftachul Arif
Department Of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Journal of Development Research

 1 
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 ., Muhammad Imamudin A'yun, Aldilla Qurrata AA Sudharmawan, AA Achmad Sabar, Achmad Ady Wicaksono Agariadne Dwinggo Samala Ahmad Barizi Ahmad Fahmi Karami Ajib Hanani Alfachruddin, M. Nabil Fahd Alfia, Lia Alfia Aqza Tri Ananda HAT Aristantia, Yuliana Aziza, Miladina Rizka A’lan Tabaika, Mokhammad Bojic, Ljubisa Cahyo Crysdian Coelho, Diogo Pereira Dedy Kurnia Setiawan Dewi Purnamasari Diah, Norizan Mat Didin Herlinudinkhaji, Didin 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 Imami, Nia Kurniawati Imamudin, M. Janitra, Geovanni Azam Juniardi Nur Fadila Junikhah, Allin Khadijah Fahmi Hayati Holle Khan, Nauman Kurniawati Kurniawati Linda Salma Angreani M. Imamudin Mauludiah, Siska Farizah Mauridhi H Purnomo Mochamad Hariadi Mochammad Wahyu Firmansyah Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faisal Muhammad Sahi Mustofa, Ahmad Habibil Nadhifah, Rizqi Aulia Nauman Khan Norizan Mat Diah Novrindah Alvi Hasanah Putra, Dony Darmawan Putra, Duvan Deswantara Rawas, Soha Ririen Kusumawati Rizqi Aulia Nadhifah Rohma, Salma Ainur Rony, Zahara Tussoleha Roro Inda Melani Safitri A Basid, Puspa Miladin Nuraida Sahi, Muhammad Santiago Criollo-C Setiyawan, Niko Heri Shoffin Nahwa Utama Sulika Sulika Supeno Mardi S. N, Supeno Mardi Supeno Mardi Susiki Nugroho, Supeno Mardi Supriyono Tarranita Kusumadewi Tsalatsatun Nur Rohmah Tsoy, Dana Wahyuliningtyas, Lia Wardani, Dyah Wibowo, Muhammad Ismail Arjun Zainal Abidin Zulfiandri Zulfiandri