Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control

doi:10.3390/bioengineering10101139

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Title:	Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control
Authors:	Liu, Mei-Yuan Liu, Chung-Feng Lin, Tzu-Chi Ma, Yu-Shan
Contributors:	Chi Mei Med Ctr, Dept Nutr Chia Nan Univ Pharm & Sci, Dept Nutr & Hlth Sci Chung Hwa Univ Med Technol, Dept Food Nutr Chi Mei Med Ctr, Dept Med Res Chi Mei Med Ctr, Nursing Dept
Keywords:	diabetes mellitus (DM) machine learning artificial intelligence feature importance predictive system glycosylated hemoglobin (HbA1c) well-controlled HbA1c diabetes-related disease nutrition education
Date:	2023
Issue Date:	2024-12-25 11:03:51 (UTC+8)
Publisher:	MDPI
Abstract:	(1) Background: Persistent hyperglycemia in diabetes mellitus (DM) increases the risk of death and causes cardiovascular disease (CVD), resulting in significant social and economic costs. This study used a machine learning (ML) technique to build prediction models with the factors of lifestyle, medication compliance, and self-control in eating habits and then implemented a predictive system based on the best model to forecast whether blood glucose can be well-controlled within 1 year in diabetic patients attending a DM nutritional clinic. (2) Methods: Data were collected from outpatients aged 20 years or older with type 2 DM who received nutrition education in Chi Mei Medical Center. Multiple ML algorithms were used to build the predictive models. (3) Results: The predictive models achieved accuracies ranging from 0.611 to 0.690. The XGBoost model with the highest area under the curve (AUC) of 0.738 was regarded as the best and used for the predictive system implementation. SHAP analysis was performed to interpret the feature importance in the best model. The predictive system, evaluated by dietitians, received positive feedback as a beneficial tool for diabetes nutrition consultations. (4) Conclusions: The ML prediction model provides a promising approach for diabetes nutrition consultations to maintain good long-term blood glucose control, reduce diabetes-related complications, and enhance the quality of medical care.
Relation:	Bioengineering-Basel, v.10, n.10, Article 1139
Appears in Collections:	[Offices] 456

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