Diabetes Prediction Based on Discrete and Continuous Mean Amplitude of Glycemic Excursions Using Machine Learning

Chronic hyperglycemia and acute glucose fluctuations are the two main factorsthat trigger complications in diabetes mellitus (DM). Continuous and sustainableobservation of thesefactors is significant to be done to reduce the potential ofcardiovascular problems in the futureby minimizing the occurrence of glycemic variability (GV). At present, observations on GV are based on the mean amplitude of glycemic excursion (MAGE), which is measured based on continuous blood glucose data from patients using particular devices. This study aims to calculate the value of MAGE based on discrete blood glucose observations from 43 volunteer patientsto predict the diabetes status of patients. Experiments were carried out by calculating MAGE values from original discrete data and continuous data obtained using Spline Interpolation. This study utilizes the machine learning algorithm, especially k-Nearest Neighbor withdynamic time wrapping (DTW) to measurethe distance between time series data. From the classification test, discrete data and continuous data from the interpolation results show precisely the sameaccuracy value that is equal to 92.85%. Furthermore, there are variations in the MAGE value for each patient where the diabetes class has the most significant difference, followed by the pre-diabetes class, and the typical class