BGEM™: Assessing Elevated Blood Glucose Levels Using Machine Learning and Wearable Photo plethysmography Sensors

Abstract

<p>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<br> 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 Women’s and Children’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.<br> We propose leveraging photoplethysmography (PPG) sensors and machine learning techniques to incorporate this into an<br> affordable wrist-worn wearable device to detect elevated blood glucose levels (⩾ 7.8mmol/L) non-invasively. Multiple machine<br> learning models were trained and assessed with 10-fold cross-validation using subject demographic data and critical features<br> extracted from the PPG measurements as predictors. Support vector machine (SVM) with a radial basis function kernel has the<br> best detection performance with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of<br> 87.51%, a geometric mean of 84.54% and F-score of 84.03%. Hence, PPG measurements can be utilized to identify subjects<br> with elevated blood glucose measurements and assist in the screening of subjects for diabetes risk.</p>

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

“…Another new study [53] utilizes wearable technology, specifically photoplethysmography (PPG) and artificial intelligence, to non-invasively monitor blood glucose levels in diabetes patients, alerting them when unhealthy levels occur.…”

“…Patient Risk stratification models assess the risk of developing certain diseases or conditions by identifying individuals at higher risk, allowing for targeted interventions and preventive measures [53].…”

“…The studies sorted according to the health product/service they support and the type of ML they use (symbol explanation: ✓means this study belongs to this health domain task). • Disease diagnosis/management healthcare sector: Approaches identified in this sector mainly belong to two studies on the supervised machine learning approach [50,53], one study on the deep learning approach [44], one study [46] on all approaches, and one more study [48] that uses statistical analysis, the backbone of ML, but does not describe an ML approach clearly. Basically, this sector uses algorithms that can be trained on labeled medical data to classify and diagnose diseases based on symptoms, medical images, or patient data.…”

“…The results can help identify high-risk patients, predict patients' results, support clinical decisions, monitor diseases, and more generally support in the health management of the population and the improvement of the supply of health care. • Telemedicine healthcare sector: Approaches identified in this sector belong to the supervised machine learning approach [52][53][54], to the reinforcement machine learning approach [51], to the supervised and deep machine learning and neural networks approaches [49], and to an unclear approach [48]. More specifically, supervised learning algorithms can be employed in telemedicine applications to analyze patient data collected remotely and provide diagnostic recommendations or monitor disease progression [52][53][54].…”

“…• Telemedicine healthcare sector: Approaches identified in this sector belong to the supervised machine learning approach [52][53][54], to the reinforcement machine learning approach [51], to the supervised and deep machine learning and neural networks approaches [49], and to an unclear approach [48]. More specifically, supervised learning algorithms can be employed in telemedicine applications to analyze patient data collected remotely and provide diagnostic recommendations or monitor disease progression [52][53][54]. Moreover, the deep learning approach can analyze data from wearable devices, detect anomalies, and provide personalized health recommendations.…”