Smart Health Care Application for Predicting Complications Risk in Type 2 Diabetes Management Using Personalized Digital Twins: A focus on early intervention and prevention strategies
Document Type
Conference Proceeding
Source of Publication
Procedia Computer Science
Publication Date
1-1-2026
Abstract
The study examined the application of Personalized Digital Twins (PDTs) to prevent complications during the management of Type 2 Diabetes, especially in early intervention and prevention plans. Based on a high-quality dataset related to the CDC Behavioral Risk Factor Surveillance System (BRFSS) data, we tested multiple predictive models such as the Random Forest, Gradient Boosting machines (GBM), and Extreme Gradient Boosting (XGBoost). We developed a composite risk indicator from established clinical risk factors (hypertension, dyslipidemia, elevated BMI) to stratify complication risk. The Random Forest model achieved 99% accuracy (AUC: 0.98) at the population-level risk classification. The GBM model was optimized and showed good results in predicting diabetes complications with precision of 0.53 and recall of 0.25 and the overall accuracy rate was 79 percent with the ROC AUC score of 0.593. Notwithstanding the positive figures, there are still issues with the model on how to improve the recall and identify true positives that are important in the management of diabetes. The present study confirms the possible benefits of machine learning in improving the management of diabetes using PDTs but also singles out the key areas requiring further improvement to increase the accuracy and reliability of the models.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
280
First Page
889
Last Page
896
Disciplines
Computer Sciences
Keywords
BRFSS dataset, Clinical Decision Support, Predictive Modeling, Random Forest, Type 2 Diabetes Complications
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Alkaabi, Haifaa and Abugabah, Ahed, "Smart Health Care Application for Predicting Complications Risk in Type 2 Diabetes Management Using Personalized Digital Twins: A focus on early intervention and prevention strategies" (2026). All Works. 8107.
https://zuscholars.zu.ac.ae/works/8107
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series