A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study

Masuda Begum Sampa; Topu Biswas; Md Siddikur Rahman; Nor Hidayati Binti Abdul Aziz; Md Nazmul Hossain; Nor Azlina Ab Aziz

doi:10.2196/49113

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study

JMIR Diabetes. 2023 Nov 24:8:e49113. doi: 10.2196/49113.

Authors

Masuda Begum Sampa^{1

2}, Topu Biswas², Md Siddikur Rahman³, Nor Hidayati Binti Abdul Aziz¹, Md Nazmul Hossain⁴, Nor Azlina Ab Aziz¹

Affiliations

¹ Center for Engineering Computational Intelligence, Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.
² Department of Computer Science and Engineering, Faculty of Science, Engineering and Technology, University of Science and Technology Chittagong, Chattogram, Bangladesh.
³ Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Bangladesh.
⁴ Department of Marketing, Faculty of Business Studies, University of Dhaka, Dhaka, Bangladesh.

PMID: 37999944
PMCID: PMC10709789
DOI: 10.2196/49113

Abstract

Background: Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information.

Objective: To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh.

Methods: Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values.

Results: Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly.

Conclusions: This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.

Keywords: blood glucose prediction; boosted decision tree regression model; machine learning; noncommunicable diseases; noninvasive.

©Masuda Begum Sampa, Topu Biswas, Md Siddikur Rahman, Nor Hidayati Binti Abdul Aziz, Md Nazmul Hossain, Nor Azlina Ab Aziz. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 24.11.2023.