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All Journal Briliant: Jurnal Riset dan Konseptual JSAI (Journal Scientific and Applied Informatics) Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Fisika dan Terapannya Indonesian Journal of Animal Agricultural Science (IJAAS) Journal of Health, Education, Economics, Science, and Technology (J-HEST) Media Ilmiah Kesehatan Indonesia Vifada Assumption Journal of Law
Jumardin, Jumardin
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Journal : JSAI (Journal Scientific and Applied Informatics)

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Perancangan Prototipe Aplikasi Prediksi Kematian Akibat Gagal Jantung Menggunakan Metode Machine Learning Berdasarkan Data Heart Failure Clinical Records Jumardin, Jumardin; Noprisson, Handrie
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7574

Abstract

This research aims to develop a prototype of the Heart Failure Death Prediction Application using machine learning methods based on clinical data from the Heart Failure Clinical Records. The application utilizes clinical patient data, such as age, blood pressure, ejection fraction, creatinine levels, and other attributes, to build a predictive model for mortality risk. Several machines learning algorithms, including Random Forest, Logistic Regression, and K-Nearest Neighbors (KNN), were employed to model and analyze the data. The dataset used in this study consists of 299 clinical records with 13 attribute columns. The target attribute is Death Event, while other attributes, such as age, gender, medical history (anemia, diabetes, high blood pressure), and laboratory test results (creatinine, sodium, and ejection fraction), were used as predictors. The application is equipped with several main menus to support its functionality, such as the Dashboard, which provides a summary of statistical prediction information and related reports, and Blog/News, which offers heart health education. The Data Master menu allows for the management of supporting data, while the Diagnosis menu is used to perform predictions based on patient input data. The Diagnosis History menu stores previous prediction results, while the Patient Data menu facilitates the management of patient information.
Co-Authors Abd. Rasyid Syamsuri Aji, Styo Mahendra Wasita Andi Syam Rizal Dg Manarang, Hidayat Fadlan Fadlan Firman Firman Fitriana Fitriana Ibrahim, Dewi Kartika Sari La Ode Nafiu Muhlis, Muhammad Yusuf Noprisson, Handrie Nurdin, Supriadi Nurul Fuadi Rahmat, Farah Alvian Ghofar Sahara Sutinah Sutinah Ulandari, Resky Ummi Kalsum Wahyullah, Wahyullah Yeni Rakhmawati, Yeni