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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika International Journal of Advances in Intelligent Informatics Jurnal Spektra Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Indonesian Physical Review Indonesian Journal of Electrical Engineering and Computer Science Policy Brief Pertanian, Kelautan, dan Biosains Tropika Jurnal Sains Materi Indonesia
Husin Alatas
Department Of Physics, Faculty Of Mathematics And Natural Sciences, IPB University, Bogor, 16680, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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Feature-enhanced residual signals with parallel hybrid network and novel loss for noninvasive blood glucose detection Mochammad Fatchur Rahman; Karlisa Priandana; Husin Alatas; Renan Prasta Jenie
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.1970

Abstract

Diabetes remains a major global health challenge. According to the International Diabetes Federation (IDF), approximately 10.5% of the world’s population is affected, with nearly half unaware of their condition. Therefore, accurate and accessible blood glucose level (BGL) monitoring remains critical. This paper presents a noninvasive BGL estimation approach using in vivo residual infrared (IR) signals collected from patients’ fingertips, which is inherently challenging due to weak glucose-related signatures and interference from other physiological factors. To address this, we propose a tailored feature engineering strategy to uncover hidden relationships between residual signals and BGL. The proposed method improves BGL range discrimination, as indicated by increased Spearman correlations and consistent gains in Kendall and Pearson correlations, demonstrating its effectiveness. Furthermore, we propose a task-specific hybrid neural network architecture, consisting of parallel 1D convolutional and dense blocks to capture both local spectral–temporal patterns and global nonlinear relationships. A distribution-aware loss function is also introduced to reduce prediction bias toward the dataset mean, improving sensitivity across the full BGL range. For validation, the proposed model is compared with random forest (RF) and boosting-based methods, showing superior performance with a 14.11% mean absolute percentage error (MAPE). Clinical reliability is evaluated using Clarke error grid (CEG) analysis, where 84.7% of predictions fall within the accurate zone and 15.3% within the clinically acceptable zone. These results demonstrate the potential of the proposed approach for practical, low-cost, and portable noninvasive BGL monitoring.
Co-Authors Adly Maulana Suherman Agus Sukarto Wismogroho Agus Sukarto Wismogroho, Agus Sukarto Akhiruddin Maddu Alfian Helmi Amigunani, Zahra Wajdini Arief Sabdo Yuwono Auzi Asfarian Cherly Firdharini Erus Rustami Fifi Gus Dwiyanti Firdharini, Cherly H. Hardhienata Hardhienata, Medria Kusuma Dewi Heru Sukoco I Dewa Made Subrata IBNUL QAYIM IPB, BPKB Irzaman, Irzaman Jayadi Jayadi Karlisa Priandana Laksono Trisnantoro Lalu Teguh Permana MAMAT RAHMAT Mochammad Fatchur Rahman Muhammad Fuad Muhammad Fuad Muhammad Iqbal Muhammad Iqbal Muhandis Siddiq Negara, Teguh Puja Peter John Morley Purbakawaca, Rady Putra, Heriansyah Renan Prasta Jenie Tantan Taopik Ramlan Tantan Taopik Ramlan, Tantan Taopik Utami, Anisa Dwi Wahyu Bambang Widayatno Wahyu Bambang Widayatno, Wahyu Bambang Yessie Widya Sari