Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation
and Monitoring Using Machine Learning and Wearable Photoplethysmography
Sensors: Algorithm Development and Validation
Abstract
Diabetes mellitus (DB) 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. This study was conducted at KK Womenâ\euro™s and
Childrenâ\euro™s Hospital of Singapore, and five hundred 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 propose leveraging photoplethysmography (PPG) sensors and machine
learning techniques to incorporate this into an affordable wrist-worn
wearable device to detect elevated blood glucose levels (⩾ 7.8mmol/L)
non-invasively. 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 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%. Hence, PPG
measurements can be utilized to identify subjects with elevated blood
glucose measurements and assist in the screening of subjects for
diabetes risk.