Ensemble Learning Regression Method for Glucose Concentration Prediction System using Colorimetric Paper-based and Smartphones

Abstract

Prediction of glucose concentration on android smartphones and colorimetric paper-based using the Ensemble learning regression model has been successfully developed. Several successful developments in our research include automatic image segmentation, image correction using the RPCC method, and the development of a regression model for urine glucose predictions. Furthermore, the model was successfully validated for best performance in the respondent's urine susceptible to color change. We used artificial urine at a 0–2000 mg/dl concentration to create a regression model based on Ensemble learning with the boosting optimization method. In addition, we also compared the Ensemble Bagging regression model and the single learner model, Decision Tree. Server-based applications were also developed using RESTful API communication with two servers: an upload server using Node.js and a computing server using the MATLAB Production Server. The testing process results using artificial urine samples showed that the performance of R2 and RRMSE were 0.98 and 0.05 for the Decision Tree and Ensemble Bagging regression models, respectively. While for the Ensemble Boosting regression model, R2 and RRMSE at the testing process are 0.98 and 0.04. The best validation results using respondents' urine samples are shown in the Ensemble Boosting regression model with R2 and RRMSE performance values of 0.97 and 0.06, respectively. The success rate of the application was 100% on both the Samsung Galaxy A51 and Huawei Nova 5T. This research estimated the glucose concentration reasonably well for health monitoring applications.