| Popis: |
Worldwide, the prevalence of diabetes has continued to increase rapidly. This gives rise to concerns regarding appropriate diabetes management to ensure optimal glycemic control. Untreated or uncontrolled diabetes can lead to a host of complications, such as cardiovascular diseases, an increased likelihood of morbidity and mortality (Deshpande, Harris-Hayes, & Schootman, 2008). A challenging problem which arises in diabetes management is the limitations of current blood glucose monitoring techniques. Electronic medical devices can potentially overcome the persistent problems in the healthcare industry. Thus, for this study, it was of interest to investigate whether advanced machine learning methods and Anura, a smartphone-based transdermal optical imaging technology (TOI) that assess health markers, can be a viable solution for diabetes management. Objectives: To examine the validity of TOI and a novel machine algorithm for diabetes prediction (i.e diabetes and non-diabetes). We compared the diabetes classification from TOI’s obtained glycated hemoglobin A1c (HbA1c) concentrations against data obtained from FDA approved blood immunoassay. Methods: In the present study, we used a kitchen sink random forest machine algorithm for diabetes prediction. The data set was obtained from 513 participants recruited during their annual physical examination at the Health Management Center of The Affiliated Hospital of Hangzhou Normal University, China. This included participant’s TOI and blood immunoassay determined HbA1c concentrations. To validate the model, pristine testing was done on 400 pristine participants pseudo randomly selected during 20 trials of training/testing. Results: The confusion matrix found TOI to have a classification accuracy of 66%, and the ROC curve of the RF classifier found TOI to have a ROC AUC of 0.69. Conclusions: The present study provides evidence for the potential use of the TOI technology, Anura, for contactless, non-invasive, and inexpensive assessments of diabetes. |
