Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier

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.

Automated summary

A non-invasive diabetes detection system based on wristband PPG signal and basic physiological parameters is proposed, achieving high accuracy with machine learning algorithms and reducing feature size and computational effort. The analysis suggests PPG signal from wearable sensors is a good alternative for non-invasive blood glucose measurements in routine applications.