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Utomo, Danang Wahyu
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : inovtek polbeng - seri informatika

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Diabetes Detection Using Stacking Technique: A Combination of XGBoost, Gradient Boosting, and Meta Model Aden Rahmat, Aden Rahmat; Wahyu Utomo, Danang
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/48asdy77

Abstract

Type 2 diabetes mellitus is a chronic and progressively increasing global health issue that necessitates early detection to mitigate serious complications such as kidney failure, neuropathy, and cardiovascular disorders. While numerous studies have developed predictive models using machine learning techniques, many are limited by their reliance on single algorithms and inadequate handling of class imbalance. This research introduces a novel strategy by employing an ensemble stacking method that integrates Gradient Boosting, XGBoost, and Random Forest, with Random Forest acting as the meta-learner. The dataset, comprising 100,000 patient records, underwent preprocessing and was balanced using the SMOTE-Tomek approach to correct class distribution disparities. The stacking process is implemented in two phases: base models generate preliminary predictions, which are subsequently used as input for the meta-model to refine the final outcomes. The evaluation demonstrates that the stacking model achieves superior performance, recording 98% accuracy and an F1-score of 0.98, outperforming the individual models. The key distinction of this study lies in the effective application of ensemble stacking to enhance prediction accuracy, especially in dealing with imbalanced and complex medical data. This methodology has the potential to improve clinical decision support systems, making them more accurate and responsive.  
Co-Authors Abu Salam Abu Salam Aden Rahmat, Aden Rahmat Agustina, Feri Aldhi Ari Kurniawan Aprilia, Nadia Eka Ardytha Luthfiarta Arifin, Muhammad Farhan Astuti, Yani Parti Astuti, Yani Parti Catur Supriyanto Catur Supriyanto Christy Atika Sari Churniansyah, Faskal Defri Kurniawan Defri Kurniawan Defri Kurniawan Defri Kurniawan Dhita Aulia Octaviani Doheir, Mohamed A S Donny Saputro Donny Saputro Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto Egia Rosi Subhiyakto, Egia Rosi Eka Putra, Zaky Dafalas Eko Hari Rachmawanto Erlin Dolphina Etika Kartikadarma Fahmi Amiq Faskal Churniansyah Finda, Selma Marsya Fitriyani, Shelomita Gusnita Darmawati Haresta, Alif Agsakli Hilmi Hanif Husin Sufi Ijayanti, Selvi Ika Novita Dewi Jullita, Efandra Eka Junta Zeniarja Kurniawan, Defri Kurniawan, Defri Liya Umaroh Maldini, Naufal Matius Rama Hadi Suryanto Muljono Muljono Ningrum, Novita Kurnia Norman, Maria Bernadette Chayeenee Novita Kurnia Ningrum Octaviani, Dhita Aulia parti astuti, yani Prajanto Wahyu Adi Prajanto Wahyu Adi, Prajanto Wahyu Pratama, Naufal Adhi Purwanto Purwanto Putri, Cindy Arlita Rahmadika Putri Tresyani Rama Hadi Suryanto, Matius Ramadhan Rakhmat Sani Riski, Habibu Rosi Subhiyakto, Egia Salsabilla, Cinta Selma Marsya Finda Sepbriant, Genta Dwigi Sindhu Rakasiwi Subhiyakto, Egia Rosi Sudibyo, Usman Tanjung, Reza Phina Tresyani, Rahmadika Putri Winarsih, Nurul Anisa Sri Yani Parti Astuti Yani Parti Astuti Zaky Dafalas Eka Putra