ItemConference Paper

A Diabetes Detection Framework Based on Datadriven Predictive Technologies

Title
A Diabetes Detection Framework Based on Datadriven Predictive Technologies
Creator
Kansal, Vandna; Upreti, Kamal; Singh, Jaspreet; Shanbhog, Manjula; Poonia, Ramesh Chandra
Description
Diabetes is a chronic disease spreading worldwide with major health challenges. It is not only caused by medical factors but other factors too such as genetic, demographics and lifestyle factors. With traditional or manual diagnosis methods, timely diagnosis becomes challenging due to complex and fragmented datasets. Recent advancements in machine learning (ML) models have greatly enhanced the efficiency and accuracy in disease diagnosis and risk evaluation. This review synthesizes the findings from the recent studies in the field of diabetes, major contributions and limitations, identifies the directions for the future work. This review has included the articles from three databases: Scopus, IEEE Xplore and PubMed; published between 2017 and 2025. The study has employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model for the review process. The scope of this work includes datadriven predictive technologies for diabetes detection, risk of complications and disease progression. It also sheds light on ongoing challenges such as data imbalance, limited interpretability, and population generalizability, while pointing to future opportunities in explainable AI and more personalized approaches to diabetes care. The review highlights that hybrid or ensemble models performing better than classical single models for risk prediction. 2025 IEEE.
Source
2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
artificial neural network; diabetes; diabetes prediction; ensemble model; machine learning; random forest; social determinants of health (SDH)
Coverage
Kansal V., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Singh J., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Shanbhog M., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Poonia R.C., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833157981-4;
Format
online
Language
English
Type
Conference paper

Citation

Kansal, Vandna; Upreti, Kamal; Singh, Jaspreet; Shanbhog, Manjula; Poonia, Ramesh Chandra, “A Diabetes Detection Framework Based on Datadriven Predictive Technologies,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26125.

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