Toward Affordable and Non-Invasive Detection of Hypoglycemia: A Machine Learning Approach

• URL: http://arxiv.org/abs/2509.17842v1
• Date: Mon, 22 Sep 2025 14:32:07 GMT
• Title: Toward Affordable and Non-Invasive Detection of Hypoglycemia: A Machine Learning Approach
• Authors: Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Vikas Ashok, Sampath Jayarathna,
• Abstract summary: This paper proposes a non-invasive method to classify states using Galvanic Skin Response (GSR), a biosignal commonly captured by wearable sensors.<n>We use the merged OhioT1DM 2018 and 2020 datasets to build a machine learning pipeline that achieves hypoglycemia and normoglycemia.<n> validation sets and 95% confidence intervals are reported to increase reliability assess.
• Score: 2.6564016286234406
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diabetes mellitus is a growing global health issue, with Type 1 Diabetes (T1D) requiring constant monitoring to avoid hypoglycemia. Although Continuous Glucose Monitors (CGMs) are effective, their cost and invasiveness limit access, particularly in low-resource settings. This paper proposes a non-invasive method to classify glycemic states using Galvanic Skin Response (GSR), a biosignal commonly captured by wearable sensors. We use the merged OhioT1DM 2018 and 2020 datasets to build a machine learning pipeline that detects hypoglycemia (glucose < 70 mg/dl) and normoglycemia (glucose > 70 mg/dl) with GSR alone. Seven models are trained and evaluated: Random Forest, XGBoost, MLP, CNN, LSTM, Logistic Regression, and K-Nearest Neighbors. Validation sets and 95% confidence intervals are reported to increase reliability and assess robustness. Results show that the LSTM model achieves a perfect hypoglycemia recall (1.00) with an F1-score confidence interval of [0.611-0.745], while XGBoost offers strong performance with a recall of 0.54 even under class imbalance. This approach highlights the potential for affordable, wearable-compatible glucose monitoring tools suitable for settings with limited CGM availability using GSR data. Index Terms: Hypoglycemia Detection, Galvanic Skin Response, Non Invasive Monitoring, Wearables, Machine Learning, Confidence Intervals.

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