JMIR Publications

ABSTRACT

Background:

Diabetes mellitus (DM) is the most challenging and fastest-growing global public health challenge. An estimated 10.5% of the global adult population suffers from diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbated the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance (IGT) and impaired fasting glycemia (IFG), respectively. All the current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or a laboratory by trained professionals. At-risk subjects might remain undetected for years and miss the precious time window for early intervention in preventing or delaying the onset of diabetes and its complications.

Objective:

We aimed to develop an AI solution to recognise elevated blood glucose levels (⩾ 7.8mmol/L) non-invasively and evaluate diabetic risk based on repeated measurements.

Methods:

This study was conducted at KK Women’s and Children’s Hospital of Singapore, and five hundred (n=500) participants were recruited (mean age 38.73 ± 10.61 years; mean BMI 24.4 ± 5.1 kg/m2). The blood glucose levels for most participants were measured before and after 75g of sugary drink using both the conventional glucometer (Accu-Chek Performa) and the wrist-worn wearable. The results obtained from the glucometer were used as the ground truth measurements. We performed extensive features engineering on the photoplethysmography (PPG) sensor data and identified features sensitive to glucose changes. These selected features were further clarified using the explainable AI approach to how these features impact our predictions.

Results:

Multiple machine learning models were trained and assessed with 10-fold cross-validation using subject demographic data and critical features extracted from the PPG measurements as predictors. Support vector machine (SVM) with a radial basis function (RBF) kernel has the best detection performance with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54% and F-score of 84.03%.

Conclusions:

Our findings suggested PPG measurements can be utilized to identify subjects with elevated blood glucose measurements and assist in the screening of subjects for diabetes risk. Clinical Trial: ClinicalTrials.gov NCT05504096; https://clinicaltrials.gov/ct2/show/NCT05504096