Machine-Learning-Based Diabetes Prediction Using Multisensor Data

Abstract

Diabetes is one such chronic disease that, if undetected, can result in several adverse symptoms or diseases. It requires continuous and active monitoring, for example, by using various smartphone sensors, wearable/smart watches, etc. These devices are minimally invasive in nature and can also track various physiological signals, which are important for the prediction of diabetes. Machine-learning algorithms and artificial intelligence are some of the most important tools used for the prediction/detection of diabetes using different types of physiological signals. In this study, we have focused on using multiple sensors such as glucose, ECG, accelerometer, and breathing sensors for classifying patients with diabetes disease. We analyzed whether a single sensor or multiple sensors can predict diabetes well. We identified various time-domain and interval-based features that are used for predicting diabetes and also the optimal window size for the feature calculation. We found that a multi-sensor combination using glucose, ECG, and accelerometer sensors gives the highest prediction accuracy of 98.2% with the xgboost algorithm. Moreover, multi-sensor prediction shows nearly 4 - 5% increase in the diabetes prediction rates as compared to single sensors. We observed that breathing-sensor-related data have very little influence on the prediction of diabetes. We also used the score-fit-times curve as one of the metrics for the evaluation of models. From the performance curves, we observed that three sensor combinations using glucose, ECG, and accelerometer converge faster as compared to a four-sensor combination while achieving with same accuracy.