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BGEM™: Assessing Elevated Blood Glucose Levels Using Machine Learning and Wearable Photo plethysmography Sensors
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  • Bohan Shi ,
  • Satvinder Singh Dhaliwa ,
  • marcus soo ,
  • Cheri chan ,
  • Jocelin Wong ,
  • Natalie W.C. Lam ,
  • Entong Zhou ,
  • Vivien Paitimusa ,
  • Kum Yin Loke ,
  • Joel Chin ,
  • Mei Tuan Chua ,
  • Kathy Chiew Suan Liam ,
  • Fadil Fatin Insyirah ,
  • Shih-Cheng Yen ,
  • Arthur Tay ,
  • Seng Bin Ang
Bohan Shi
Actxa, Actxa

Corresponding Author:[email protected]

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Satvinder Singh Dhaliwa
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marcus soo
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Cheri chan
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Jocelin Wong
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Natalie W.C. Lam
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Entong Zhou
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Vivien Paitimusa
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Kum Yin Loke
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Joel Chin
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Mei Tuan Chua
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Kathy Chiew Suan Liam
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Fadil Fatin Insyirah
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Shih-Cheng Yen
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Arthur Tay
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Seng Bin Ang
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Abstract

Diabetes mellitus (DB) is the most challenging and fastest-growing global public health challenge. An estimated 10.5% of the global adult population has been suffering from diabetes, and almost half of them are undiagnosed. The growing at-risk population further exacerbated the scarce health resources where the adults worldwide with impaired glucose tolerance (IGT) and impaired fasting glycaemia (IFG) were estimated at around 10.6% and 6.2%, 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 Women’s and Children’s Hospital of Singapore, and five hundred participants were recruited (mean age 38.73 $\pm$ 10.61 years; mean BMI 24.4 $\pm$ 5.1 kg/m\textsuperscript{2}). 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 ($\geqslant 7.8 mmol/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.
27 Oct 2023Published in JMIR AI volume 2 on pages e48340. 10.2196/48340