Department of Endocrinology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Department of Emergency Medicine Stanford Healthcare TriValley, Stanford University School of Medicine, Stanford, Pleasanton, CA, United States.
Front Endocrinol (Lausanne). 2023 Jan 20;13:1061507. doi: 10.3389/fendo.2022.1061507. eCollection 2022.
For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurement serves as a good source of data to train machine learning models for prediction purposes. This study aimed at using machine learning methods to predict blood glucose for type 2 diabetic patients. We investigated various parameters influencing blood glucose, as well as determined the most effective machine learning algorithm in predicting blood glucose.
273 patients were recruited in this research. Several parameters such as age, diet, family history, BMI, alcohol intake, smoking status et al were analyzed. Patients who had glycosylated hemoglobin less than 6.5% after 52 weeks were considered as having achieved glycemic control and the rest as not achieving it. Five machine learning methods (KNN algorithm, logistic regression algorithm, random forest algorithm, support vector machine, and XGBoost algorithm) were compared to evaluate their performances in prediction accuracy. R 3.6.3 and Python 3.12 were used in data analysis.
The statistical variables for which p< 0.05 was obtained were BMI, pulse, Na, Cl, AKP. Compared with the other four algorithms, XGBoost algorithm has the highest accuracy (Accuracy=99.54% in training set and 78.18% in testing set) and AUC values (1.0 in training set and 0.68 in testing set), thus it is recommended to be used for prediction in clinical practice.
When it comes to future blood glucose level prediction using machine learning methods, XGBoost algorithm scores the highest in effectiveness. This algorithm could be applied to assist clinical decision making, as well as guide the lifestyle of diabetic patients, in pursuit of minimizing risks of hyperglycemic or hypoglycemic events.
对于 2 型糖尿病患者，血糖水平可能受到多种因素的影响。准确估计血糖轨迹对于临床决策至关重要。频繁测量血糖可以为机器学习模型的预测提供良好的数据来源。本研究旨在使用机器学习方法预测 2 型糖尿病患者的血糖。我们研究了影响血糖的各种参数，并确定了预测血糖的最有效机器学习算法。
本研究纳入了 273 名患者。分析了年龄、饮食、家族史、BMI、酒精摄入、吸烟状况等多个参数。将 52 周后糖化血红蛋白<6.5%的患者视为血糖控制达标，其余患者视为未达标。比较了 5 种机器学习方法（KNN 算法、逻辑回归算法、随机森林算法、支持向量机和 XGBoost 算法）的预测准确性。数据分析使用 R 3.6.3 和 Python 3.12。
p<0.05 的统计学变量为 BMI、脉搏、Na、Cl、AKP。与其他四种算法相比，XGBoost 算法的准确率最高（训练集准确率为 99.54%，测试集准确率为 78.18%）和 AUC 值最高（训练集 AUC 值为 1.0，测试集 AUC 值为 0.68），因此建议在临床实践中用于预测。
在使用机器学习方法预测未来血糖水平方面，XGBoost 算法的效果最佳。该算法可用于辅助临床决策，并指导糖尿病患者的生活方式，以最大程度地降低高血糖或低血糖事件的风险。
