An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data

Rosy Oh, Hong Kyu Lee, Youngmi Kim Pak and Man-Suk Oh
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Rosy Oh: Department of Mathematics, Korea Military Academy, Seoul 01805, Korea
Hong Kyu Lee: Department of Internal Medicine, College of Medicine, Seoul National University, Seoul 03080, Korea
Youngmi Kim Pak: Department of Physiology, College of Medicine, Kyung Hee University, Seoul 02447, Korea
Man-Suk Oh: Department of Statistics, Ewha Womans University, Seoul 03760, Korea

IJERPH, 2022, vol. 19, issue 10, 1-17

Abstract: The early prediction and identification of risk factors for diabetes may prevent or delay diabetes progression. In this study, we developed an interactive online application that provides the predictive probabilities of prediabetes and diabetes in 4 years based on a Bayesian network (BN) classifier, which is an interpretable machine learning technique. The BN was trained using a dataset from the Ansung cohort of the Korean Genome and Epidemiological Study (KoGES) in 2008, with a follow-up in 2012. The dataset contained not only traditional risk factors (current diabetes status, sex, age, etc.) for future diabetes, but it also contained serum biomarkers, which quantified the individual level of exposure to environment-polluting chemicals (EPC). Based on accuracy and the area under the curve (AUC), a tree-augmented BN with 11 variables derived from feature selection was used as our prediction model. The online application that implemented our BN prediction system provided a tool that performs customized diabetes prediction and allows users to simulate the effects of controlling risk factors for the future development of diabetes. The prediction results of our method demonstrated that the EPC biomarkers had interactive effects on diabetes progression and that the use of the EPC biomarkers contributed to a substantial improvement in prediction performance.

Keywords: diabetes mellitus; glucose intolerance; machine learning; Bayesian network; environmental pollutants (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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