한국 중장년 여성에 대한 기계학습 기반 비침습적 요인들을 이용한 당뇨 및 공복혈당 장애 분류 = Classification of Diabetes and Impaired Fasting Glucose using Noninvasive Factors based on Machine Learning Approaches in Korean Middle Aged Women

다국어 초록 (Multilingual Abstract)

This study aimed to build models to classify diabetes and impaired fasting glucose requiring active management of blood sugar based on machine learning approaches using noninvasive variables, and to evaluate the performance of each model. The classification models of diabetes and impaired fasting glucose in a total of 215 women aged 40 to 69 were built through six machine learning approaches. The performance of each model was evaluated using nested cross-validation. The model using elastic net logistic regression reported slightly higher performance. The area of diastolic period and standard deviation of pulse rate were founded to be relatively important variables in diabetes and impaired fasting glucose. These results showed the potential of noninvasive variables for the classification of diabetes and impaired fasting glucose. Also, classification based on machine learning approaches can help clinicians make clinical decisions and provide healthcare services.

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