A Machine Learning–Based Approach to Noninvasively Detect Hypoglycemia from Gaze Behavior While Driving

Abstract

Aim: To non-invasively detect hypoglycemia in individuals with type 1 diabetes (T1D) based on gaze behavior while driving. Methods: Controlled hypoglycemia was induced in 19 individuals (12 males, age 32 ± 7.1 yrs) with T1D (HbA1c 7.1 ± 0.6% [54 ± 6 mmol/mol]) using an adapted hypoglycemic clamp protocol. Gaze and blood glucose (BG) data were gathered while driving in a simulator during three 18 min sessions: session 1 (BG 90-144 mg/dL), session 2 (BG declining from 72 to 45 mg/dL), and session 3 (BG 36-45 mg/dL). A gradient-boosting machine learning (ML) model was built for hypoglycemia (BG < 70 mg/dL) detection based on gaze behavior. Results: Mean venous BG was 105.4 ± 11.4 mg/dL during session 1, declined from 61.4 ± 6.1 mg/dL to 47.2 ± 8.5 mg/dL during session 2, and was 42.7 ± 4.1 mg/dL during session 3, respectively. Gaze analysis provided 29,968 data samples (1,577.5 ± 52 per subject, 10,041 euglycemia, 19,927 hypoglycemia). Overall, ML achieved an area under the receiver-operating-characteristics curve of 0.83 ± 0.09 for hypoglycemia detection with leave-one-subject-out cross-validation. Conclusion: ML-based gaze analysis shows high accuracy in non-invasive hypoglycemia detection while driving. Our approach offers promising potential in various settings where cameras are available.