Improving the efficiency of machine learning models for predicting blood glucose levels and diabetes risk

Abstract

Fasting blood glucose is used as an indicator in the process of predicting diabetes risk. This research aims to, i) create a model for predicting blood glucose level using data mining algorithms, ii) a selection algorithm was used to select a feature from the correlation of the data, and iii) to compare the model's performance with the classical methods. All clinical data ware recorded and compiled in a database by hospital staff from 2014-2019. In our previous research, the blood glucose prediction model had an acceptable accuracy where 18 patient features were used as input data to the data mining process. In this research, we demonstrated that the random forest classifier and extra tree classifier algorithms have an outstanding in discarding non-critical attributes. And the process of reducing the number of those features has impacted the glycemic prediction model with higher efficiency. Seventeen machine learning algorithms are used to find the best performance models. Our results clearly show that the improved prediction model is more efficient. This experiment has shown that improvements to our proposed model were able to predict blood glucose levels with 99.69% and 99.63% accuracy for random forest classifier, extra tree classifier, and Gaussian process classifier, respectively.