Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial

Abstract

BackgroundType 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM.ObjectiveThe objective of this work was to dynamically forecast daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before.MethodsWe used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory–based recurrent neural networks to forecast the next-day glucose levels in individual patients. The neural network used several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbohydrates; exercise; and weight) were progressing from one day to another from noisy data.ResultsThe model was validated based on a data set of 10 patients who had been monitored daily for over 6 months. The proposed deep learning model demonstrated considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values.ConclusionsUsing machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to better predict future glucose levels for T2DM management.

Prediction of fasting plasma glucose and glycated haemoglobin using machine learning based on tongue features

“…A recent cross-sectional study conducted in USA showed that machine learning models based on survey questionnaires are able to identify individuals at high risk of diabetes [46]. In another study [32], a deep learning model for dynamically predicting blood glucose was developed, which is conducive to selfmanagement of diabetes. Machine learning algorithms have become a key process for mining the internal relationship between clinical data and diabetes or pre-diabetes [47].In our study, classical machine learning models with different structures are used to explore the linear relationship between tongue features and glucose metabolism indicators, moreover, we attempted to enhance this relationship through model fusion.…”

“…We should not only consider the actual working accuracy of the prediction model, but also consider the in uence of the prediction model on the clinical decision. Consequently, the Clark's Error Grid Analysis(EGA) [32,33] was used to determine the acceptable error for the accuracy of predictive value of FPG in comparison with the actual value. The more values that appear in Zones A and B, the more accurate the model is in terms of clinical utility [34].The scatter-plot was used to access the performance of the HbA 1c prediction model [35].…”

Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data

“…Machine learning, according to their regular physical examination results, can help people make a preliminary decision about DM and it can serve as a guide for doctors [18][19][20]. Applying machine learning and data mining methods in DM research is a crucial approach for using vast amounts of available diabetes-related data for information extraction [21][22][23]. The severe social impact of the specific disease makes DM one of the top priorities of medical science research, which eventually generates vast amounts of data.…”

Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation

“…In the last few years, deep learning algorithms, especially artificial neural networks (ANNs), have significantly enhanced the performance of artificial intelligence–based CAD tools, which are used for diagnostic purposes in various medical domains [ 11 - 15 ]. In general, these ANN models undergo a training procedure to learn the optimal representation of the training data set [ 16 ] by using optimization algorithms, such as stochastic gradient descent [ 17 ].…”