Liu Ying-Qiang, Chang Tzu-Wei, Lee Lung-Chun, Chen Chia-Yu, Hsu Pi-Shan, Tsan Yu-Tse, Yang Chao-Tung, Chu Wei-Min
Department of Medical Education, Taichung Veterans General Hospital, Taichung 407219, Taiwan.
Department of Family Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan.
Diagnostics (Basel). 2024 Dec 31;15(1):72. doi: 10.3390/diagnostics15010072.
: The prevalence of diabetes is increasing worldwide, particularly in the Pacific Ocean island nations. Although machine learning (ML) models and data mining approaches have been applied to diabetes research, there was no study utilizing ML models to predict diabetes incidence in Taiwan. We aimed to predict the onset of diabetes in order to raise health awareness, thereby promoting any necessary lifestyle modifications and help mitigate disease burden. : The research dataset used in the study was retrieved from the Clinical Data Center of Taichung Veterans General Hospital. We collected data from the available electronic health records with a total of 33 items being employed for model construction. Individuals with diabetes and those with missing data were excluded. Ultimately, 6687 adults were included in the final analysis, where we implemented three different ML algorithms, including logistic regression (LR), random forest (RF) and extreme gradient boosting (XGBoost) in order to predict diabetes. : The top five important factors involved in the prediction model were glycated hemoglobin (HbA1c), fasting blood glucose, weight, free thyroxine (fT4), and triglycerides (TG). Notably, random forest, logistic regression, and XGBoost reached 99%, 99%, and 98% accuracy, respectively. fT4 seems to be one of the significant features in predicting the onset of diabetes. Moreover, this would be the first study using machine learning models to predict diabetes that has demonstrated the importance of thyroid hormone. : A total of 33 items were able to be put into the machine learning model in order to predict diabetes with promising accuracy. In comparison to prior studies on machine learning models, this study not only identified similar key factors for predicting diabetes but also highlighted the significance of thyroid hormones, a factor that was previously overlooked. Moreover, it highlighted the relevance of predicting type 2 diabetes using more affordable methods, which would be useful for clinical healthcare professionals and endocrinologists who apply the models to clinical practice.
糖尿病在全球范围内的患病率正在上升，尤其是在太平洋岛国。尽管机器学习（ML）模型和数据挖掘方法已应用于糖尿病研究，但在台湾尚无利用ML模型预测糖尿病发病率的研究。我们旨在预测糖尿病的发病情况，以提高健康意识，从而促进必要的生活方式改变，并有助于减轻疾病负担。
本研究使用的研究数据集取自台中荣民总医院临床数据中心。我们从可用的电子健康记录中收集数据，共33项用于模型构建。排除患有糖尿病和数据缺失的个体。最终，6687名成年人被纳入最终分析，我们实施了三种不同的ML算法，包括逻辑回归（LR）、随机森林（RF）和极端梯度提升（XGBoost）来预测糖尿病。
预测模型中涉及的前五个重要因素是糖化血红蛋白（HbA1c）、空腹血糖、体重、游离甲状腺素（fT4）和甘油三酯（TG）。值得注意的是，随机森林、逻辑回归和XGBoost的准确率分别达到了99%、99%和98%。fT4似乎是预测糖尿病发病的重要特征之一。此外，这将是第一项使用机器学习模型预测糖尿病的研究，该研究证明了甲状腺激素的重要性。
总共33项能够被纳入机器学习模型以预测糖尿病，准确率很有前景。与之前关于机器学习模型的研究相比，本研究不仅确定了预测糖尿病的类似关键因素，还强调了甲状腺激素的重要性，而这一因素此前被忽视。此外，它强调了使用更经济实惠的方法预测2型糖尿病的相关性，这对将模型应用于临床实践的临床医疗专业人员和内分泌学家很有用。
