학술논문
머신러닝 기법을 활용한 유산소 운동 중 혈당 변화 예측 모형
Prediction Models of Blood Glucose Change During Aerobic Exercise Using Machine Learning Techniques
Prediction Models of Blood Glucose Change During Aerobic Exercise Using Machine Learning Techniques
Document Type
Article
Author
낙기광 / Okimitsu Oyama; 최성규 / Seonggyu Choi; 오창근 / Changgeun Oh; 김은찬 / Eunchan Kim; 박동혁 / Dong-hyuk Park; 오민석 / Minsuk Oh; 박대현 / Dae-hyun Park; 서혜경 / Hye-kyoung Seo; 한정선 / Jungsun Han; 전동재 / Dongiae Jeon; 김성혁 / Seong-hyok Kim; 전용관 / Justin Y Jeon
Source
운동과학 / Official Journal of the Korea Exercise Science Academy. Aug 31, 2023 32(3):295
Subject
Language
Korean
ISSN
1226-1726
Abstract
PURPOSE: This study aimed to explore the relationship between blood glucose level changes and body characteristics during exercise and to present six models for predicting changes in blood glucose levels during exercise. METHODS: 148 healthy men and women (age: 31.9±9.7 year, fasting blood glucose: 102.1±14.1 mg/dL, p=.032) participated in the study, and 30 of them participated in the study. Eight variables were selected to build two prediction models: 24-hour ingested carbohydrates, age, blood glucose, heart rate changes, sex, skeletal muscle mass, heart rate recovery after exercise, and resting heart rate. Logistic regression and random forest classifier models were used to predict the changes in blood glucose levels during exercise. RESULTS: A total of six models were created for all participants, male and female. Random forest classification (training set: AUC=0.837, Youden index=0.66; validation set: AUC=0.730, Youden index=0.53) and logistic regression classification models (training set: AUC=0.807, Youden index=0.55; validation set: AUC=0.735, Youden index=0.57) were built. CONCLUSION: The random forest model showed good performance in classifying internal data, whereas the logistic regression classification model demonstrated relatively good performance in classifying validation data.