{"ID":2867754,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17842","arxiv_id":"2509.17842","title":"Toward Affordable and Non-Invasive Detection of Hypoglycemia: A Machine Learning Approach","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 \u003c 70 mg/dl) and normoglycemia (glucose \u003e 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.","short_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 cla...","url_abs":"https://arxiv.org/abs/2509.17842","url_pdf":"https://arxiv.org/pdf/2509.17842v1","authors":"[\"Lawrence Obiuwevwi\",\"Krzysztof J. Rechowicz\",\"Vikas Ashok\",\"Sampath Jayarathna\"]","published":"2025-09-22T14:32:07Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}