Design and Statistical Evaluation of an AI-Enabled IoT-Based Non-Invasive Biosensing System for Diabetes Risk Screening - Prachi C. Kamble; Lakshamappa Ragha; Yogesh Pingle (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2026)
Early identification of diabetes risk remains a significant challenge due to the invasive nature, recurring cost, and limited accessibility of conventional biochemical diagnostic tests. These limitations restrict continuous monitoring and hinder large-scale population screening, particularly in remote and resource-limited settings. The aim of this study is to design and statistically evaluate an AI-enabled IoT-based non-invasive biosensing system for diabetes risk screening, focusing on system-level engineering design, data integration, and performance validation rather than clinical diagnosis. In this study, the term “non-invasive” refers exclusively to externally measurable surface-level physiological and breath-based signals that do not require skin penetration, blood sampling, or subdermal sensor implantation. The main contributions of this work include the development of a wearable IoT-based non-invasive biosensing framework, integration of multi-modal physiological and breath-based biomarkers for risk assessment, implementation of an ensemble machine learning model for diabetes risk classification, and comprehensive statistical validation using agreement, reliability, and calibration metrics. The proposed DiaAssist system acquires physiological parameters such as heart rate, blood pressure, oxygen saturation, body temperature, physical activity indicators, and breath volatile organic compound acetone through a wearable IoT platform with edge-level preprocessing. Fused physiological and demographic features are processed using an ensemble learning framework to generate individualized diabetes risk scores. Performance evaluation was conducted on a single-center observational dataset comprising 625 records using paired statistical tests, agreement analysis, and calibration assessment. The optimized model achieved an accuracy of 99.7%, an area under the receiver operating characteristic curve of 1.000, a Cohen’s Kappa coefficient of 0.993, a Matthews correlation coefficient of 0.993, and a Brier score of 0.045, demonstrating strong classification reliability and probabilistic calibration. The results confirm that combining IoT-based non-invasive biosensing with ensemble machine learning enables accurate and reliable screening for diabetes risk. The proposed system provides a scalable, cost-effective, and engineering-oriented solution suitable for remote monitoring and preventive healthcare applications