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All Journal Jurnal Simantec International Journal of Advances in Intelligent Informatics Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi Jurnal ULTIMATICS Jurnal ULTIMA Computing Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Brilliance: Research of Artificial Intelligence NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Digital Transformation Technology (Digitech) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer AL-UMRON: Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian Masyarakat dan Riset Pendidikan Almantiq
Irsyada, Rahmat
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Predicting Heart Failure Status Using Binary Logistic Regression with Clinical and Demographic Factors: Penelitian Cahyani, Nita; Irsyada, Rahmat
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5189

Abstract

The aim of this study was to identify clinical and demographic characteristics associated with heart failure and develop an interpretable risk model using binary logistic regression on hospital patient data. Early detection of heart failure is expected to support timely intervention and clinical decision-making based on routine measurements. This study analyzed 130 anonymized patient data with heart failure status as a binary outcome. The initial logistic regression model included all candidate predictors and was then simplified to improve stability and calibration. Results are presented as odds ratios with 95% CIs. Performance evaluation included ROC–AUC, classification metrics, the Hosmer–Lemeshow test, calibration plots, and 5-fold cross-validation. The final model was significant (LR p = 1.0×10⁻⁵; McFadden R² = 0.222) with an accuracy of 81.54%, sensitivity of 89.41%, specificity of 66.67%, AUC of 0.811, and a Brier score of 0.164. Cross-validation showed an average AUC of 0.774 and an accuracy of 0.762. Significant predictors included BMI, serum creatinine, serum potassium, and total cholesterol, with acceptable calibration (p = 0.0767). This model has potential use as an interpretive screening tool, although external validation is still needed.
Co-Authors Agus Sulistiawan Ahmad Dardiri Aji Prasetya Wibawa Akhmad Arif Setiawan Alfiyatul, Siti Nur Audytra, Hastie Dhaniyar Dhaniyar Diantika, Devi Eka Didik Abdul Mukmin Doni Abdul Fatah Erick Febriyanto Hilmi Alriad Indah Listyorini Izudin, Asfa Reza Joko Riyadi Juwita Annisa Fauzi Kartini, Alif Yuanita Lailatul Badriyah Leonel Hernandez M.Pd S.T. S.Pd. I Gde Wawan Sudatha . Mahmuda, Rahmawati Muhdlor Auhal Haq Mu’afa , M Rif’an Fawajul Naila Afina Rohmah Nita Cahyani Nurul Hafidhin Perdana , Chepy Prima Angga Hadi Saputra Putri Liana Putri, Wardiana Adinda Roihatur Rohmah Rubaiyah, Zumrotul Sanjaya, Ucta Pradema Seno Isbiyantoro Sunu Wahyudhi Syaifuddin Syaifuddin Teguh Pribadi Ulfa Ulfa Umi Kholifah Yundari, Yundari