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Muhammad Syahriandi Adhantoro
Faculty Of Communication And Informatics, Universitas Muhammadiyah Surakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Bulletin of Electrical Engineering and Informatics

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A robust model for early detection of chronic kidney disease leveraging machine learning and data balancing techniques Imaduddin, Helmi; Yusuf, Siti Agrippina Alodia; Adhantoro, Muhammad Syahriandi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11247

Abstract

Chronic kidney disease (CKD) requires reliable early screening, yet clinical datasets are often highly class imbalanced, which can bias machine learning models and reduce detection quality. This study presents a unified evaluation of two imbalance mitigation strategies, synthetic minority over-sampling technique (SMOTE), and cost-sensitive learning, across six classifiers: decision tree (DT), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Experiments were conducted on a public CKD dataset with 1,659 records and 54 features using a consistent pipeline including preprocessing, feature selection, imbalance handling, and stratified k-fold cross-validation. Models were assessed with accuracy, precision, recall, and F1-score. Results show that the imbalance strategy materially changes model behavior: cost-sensitive learning generally improves precision, while SMOTE more often increases recall and F1-score. The best overall performance was achieved by XGBoost with cost-sensitive learning, reaching 93% accuracy and 92% precision, outperforming prior reports on the same dataset. RF remained stable across both strategies, whereas KNN was sensitive to SMOTE induced distribution shifts. These findings provide practical guidance for selecting imbalance handling methods to improve healthcare machine learning for CKD detection.
Co-Authors Adi Jufriansah Al-Kubro, Putri Balqis Alas, Yabit Anam Sutopo Andiyani, Rhavida Anniza Aninditawidagda Pandam Sudaryanto Astuti, Dyah Baihaqi, Muhamad Adzib Bakri, Yuhanis Mhd Bambang Sumardjoko Chandra Anugrah Putra D Djumadi Devary Pradana Setyabudi Dian Artha Kusumaningtyas Dody Hartanto Dyah Astuti Eko Purnomo Faris Athoil Haq Fitriany, Devinda Nur Gamal Abdul Nasir Zakaria Hanifah, Dini Haq, Muhammad Izzul Harun Joko Prayitno Helmi Imaduddin Hernanjaya, Arif Nugraha Hernawan Sulistyanto Ishafit Ishafit Jumadi Jumadi Kurniaji, Ganno Tri Buana Kurniaji, Ganno Tribuana Kurniasari, Efi Lenny Puspita Dewi Markhamah Moh. Irma Sukarelawan Muhammad Izzul Haq Muhammad Qolbi Shobri Muhammad Rohmadi Muhroji Mutiara Sari, Ananda Naufal Ishartono Nova Tri Romadloni Pradana, Fajar Gemilang Ratri, Dyan Permata Sabar Narimo Setyabudi, Devary Pradana Setyawati, Yayuk Sofyan Anif Sopianti, Herlin Ulya, Walidatul Umardhani, Niluh Sekar Zulfa Wahyu Nanda Eka Saputra, Wahyu Nanda Eka YAN ADITYA PRADANA Yasir Sidiq Yusof, Norazmie Yusuf, Siti Agrippina Alodia Zakaria, Gamal Abdul Nasir