Purpose: The prevalence of diabetes mellitus (DM) continues to increase worldwide. We built a machine learning model and developed a prediction system that is based on an optimal model to effectively predict blood sugar changes in patients with diabetes. Our findings contribute to the implementation of long-term patient nutrition interventions.
Method: Data of outpatients with type 2 DM who were 20 years or older and underwent nutrition education under a diabetes pay-for-performance program were obtained from the Nutrition and Health System Database of the outpatient clinic of the Chi Mei Hospital network; the data spanned the years from 2007 to 2019. On the basis of literature findings and professional experience, 20 characteristic variables and multiple machine learning algorithms were applied to build a model to predict whether the glycosylated hemoglobin (HbA1c) of the outpatients improved by more than 7% after 1 year. The optimal model (model with the highest area under the curve [AUC]) was selected and used to develop a prediction system for use in clinical settings.
Results: The accuracy levels of the developed models ranged from 0.735 to 0.749; the supportvector- machine model with a sensitivity of 0.757, a specificity of 0.739, and an AUC of 0.828 was the optimal prediction model. The prediction system was tested by three dietitians, who affirmed its usefulness for diabetes meal planning and patient health education.
Conclusion: The prediction model based on machine learning algorithms performed excellently, and it is a promising tool for diabetes meal planning and patient health education. It is also an effective supporting tool for clinical disease care and dietary health education interventions. We believe that the model can help patients maintain favorable long-term blood sugar control, reduce their incidence of diabetes-related complications, improve the quality of medical care and promote shared decisionmaking.
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