Non-invasive early diabetes prediction has been gaining much premarkable over the last decade. Heart rate variability (HRV) is the only non-invasive technique that can predict the future occurrence of the disease. Early prediction of diabetes can help doctors start an early intervention. To this end, the authors have developed a computational machine learning model to predict type 2 diabetes mellitus (T2DM) risk using heart rate variability features and have evaluated its robustness against the HRV of 50 patients data. The electrocardiogram (ECG) signal of the control population (n=40) and T2DM population (n=120) have been recorded in the supine position for 5 minutes, and HRV signals have been obtained. The time domain, frequency domain, and non-linear features have been extracted from the HRV signal. A decision support system has been developed based on a machine learning algorithm. Finally, the decision support system has been validated using the HRV features of 50 patients (Control n=10 and T2DM n=40). HRV features are selected for the prediction of T2DM. The decision support system has been designed using three machine learning models: Gradient boosting decision tree (GBDT), Extreme Gradient boosting (XGBoost), Categorical boosting (CatBoost), and their performance have been evaluated based on the Accuracy (ACC), Sensitivity (SEN), Specificity (SPC), Positive predicted value (PPV), Negative predicted value (NPV), False-positive rate (FPR), False-negative rate (FNR), F1 score, and Area under the receiver operating characteristic curve (AUC) metrics. The CatBoost model offers the best performance outcomes, and its results have been validated on 50 patients. Thus the CatBoost model can be use as a decision support system in hospitals to predict the risk of T2DM.

中文翻译：

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基于机器学习模型的使用心率变异性特征预测 T2DM 风险的实现

在过去的十年中，非侵入性早期糖尿病预测取得了显着的进展。心率变异性（HRV）是唯一可以预测疾病未来发生的非侵入性技术。糖尿病的早期预测可以帮助医生开始早期干预。为此，作者开发了一种计算机器学习模型，以使用心率变异性特征预测 2 型糖尿病 (T2DM) 风险，并针对 50 名患者的 HRV 数据评估其稳健性。对照人群（n=40）和T2DM人群（n=120）在仰卧位5分钟记录心电图（ECG）信号，获得HRV信号。从 HRV 信号中提取了时域、频域和非线性特征。已经开发了基于机器学习算法的决策支持系统。最后，决策支持系统已经使用 50 名患者（对照组 n=10 和 T2DM n=40）的 HRV 特征进行了验证。选择 HRV 特征来预测 T2DM。决策支持系统使用三种机器学习模型设计：梯度提升决策树（GBDT）、极限梯度提升（XGBoost）、分类提升（CatBoost），并基于准确度（ACC）、灵敏度（ SEN)、特异性 (SPC)、阳性预测值 (PPV)、阴性预测值 (NPV)、假阳性率 (FPR)、假阴性率 (FNR)、F1 分数和受试者工作特征曲线下面积 ( AUC) 指标。CatBoost 模型提供了最佳的性能结果，其结果已在 50 名患者身上得到验证。因此，CatBoost 模型可以用作医院的决策支持系统来预测 T2DM 的风险。