In this work, a non-invasive diabetes mellitus detection system is proposed based on the wristband photoplethysmography (PPG) signal and basic physiological parameters (PhyP) to enable easy detection of diabetes mellitus (DM). A dataset of 217 participants with diabetes, prediabetes and normal conditions is used to develop the system. The Mel frequency cepstral coefficients (MFCC) extracted from 5s PPG signal segments and the PhyP are used as input for the machine learning algorithms. The K-nearest neighbors, support vector machine, random forest and extreme gradient boost (XGBoost) classifiers are used for classification. In addition, a hybrid feature selection method (Hybrid FS) is proposed to reduce the size of the input data. The Hybrid FS-based XGBoost system achieves a high accuracy of 99.93 % for non-invasive diabetes detection with fewer features and less computational effort. The analysis suggests that the PPG signal from a wearable sensor is a good alternative for simple non-invasive blood glucose measurements in routine applications.

在这项工作中，提出了一种基于腕带光电容积描记 (PPG) 信号和基本生理参数 (PhyP) 的无创糖尿病检测系统，以轻松检测糖尿病 (DM)。使用 217 名患有糖尿病、糖尿病前期和正常情况的参与者的数据集来开发该系统。从 5s PPG 信号段中提取的 Mel 频率倒谱系数 (MFCC) 和 PhyP 用作机器学习算法的输入。K-最近邻、支持向量机、随机森林和极端梯度提升（XGBoost）分类器用于分类。此外，还提出了一种混合特征选择方法（Hybrid FS）来减少输入数据的大小。基于Hybrid FS的XGBoost系统达到了99的高精度。93% 用于非侵入性糖尿病检测，具有更少的特征和更少的计算工作。分析表明，来自可穿戴传感器的 PPG 信号是常规应用中简单无创血糖测量的良好替代方案。