Internal Medicine Department, Mostoles University Hospital, Rey Juan Carlos University, Calle Rio Jucar, s/n, 28935, Mostoles (Madrid), Spain.
Department of Signal Theory and Communications and Telematics Systems and Computing, Rey Juan Carlos University, Mostoles, Spain.
Med Biol Eng Comput. 2020 May;58(5):991-1002. doi: 10.1007/s11517-020-02132-w. Epub 2020 Feb 26.
Prediabetes is a type of hyperglycemia in which patients have blood glucose levels above normal but below the threshold for type 2 diabetes mellitus (T2DM). Prediabetic patients are considered to be at high risk for developing T2DM, but not all will eventually do so. Because it is difficult to identify which patients have an increased risk of developing T2DM, we developed a model of several clinical and laboratory features to predict the development of T2DM within a 2-year period. We used a supervised machine learning algorithm to identify at-risk patients from among 1647 obese, hypertensive patients. The study period began in 2005 and ended in 2018. We constrained data up to 2 years before the development of T2DM. Then, using a time series analysis with the features of every patient, we calculated one linear regression line and one slope per feature. Features were then included in a K-nearest neighbors classification model. Feature importance was assessed using the random forest algorithm. The K-nearest neighbors model accurately classified patients in 96% of cases, with a sensitivity of 99%, specificity of 78%, positive predictive value of 96%, and negative predictive value of 94%. The random forest algorithm selected the homeostatic model assessment-estimated insulin resistance, insulin levels, and body mass index as the most important factors, which in combination with KNN had an accuracy of 99% with a sensitivity of 99% and specificity of 97%. We built a prognostic model that accurately identified obese, hypertensive patients at risk for developing T2DM within a 2-year period. Clinicians may use machine learning approaches to better assess risk for T2DM and better manage hypertensive patients. Machine learning algorithms may help health care providers make more informed decisions.
糖尿病前期是一种高血糖症，患者的血糖水平高于正常但低于 2 型糖尿病（T2DM）的阈值。糖尿病前期患者被认为患 T2DM 的风险较高，但并非所有人最终都会患病。由于难以确定哪些患者患 T2DM 的风险增加，我们开发了一种基于多种临床和实验室特征的模型，以预测 2 年内 T2DM 的发生。我们使用有监督的机器学习算法从 1647 名肥胖、高血压患者中识别出高危患者。研究期间从 2005 年开始，到 2018 年结束。我们将数据限制在 T2DM 发生前 2 年。然后，使用每个患者特征的时间序列分析，我们为每个特征计算了一条线性回归线和一条斜率。随后，将特征纳入 K 最近邻分类模型。使用随机森林算法评估特征重要性。K 最近邻模型在 96%的情况下准确分类患者，敏感性为 99%，特异性为 78%，阳性预测值为 96%，阴性预测值为 94%。随机森林算法选择稳态模型评估-估计胰岛素抵抗、胰岛素水平和体重指数作为最重要的因素，这些因素与 KNN 结合后，准确率为 99%，敏感性为 99%，特异性为 97%。我们构建了一个预后模型，能够准确识别出在 2 年内有发展为 T2DM 风险的肥胖、高血压患者。临床医生可能会使用机器学习方法来更好地评估 T2DM 的风险，并更好地管理高血压患者。机器学习算法可能有助于医疗保健提供者做出更明智的决策。
