InSight-D Through the Lens of AI: A Comprehensive Review of Traditional Techniques and Future-Ready Frameworks

DOI

10.2991/978-94-6463-858-5_102How to use a DOI?

Keywords

Diabetes mellitus (DM); machine learning; Random Forest (RF); K-Nearest Neighbor (KNN); Support Vector Machine (SVM); early diabetes prediction; Artificial intelligence(AI)

Abstract

Diabetes is a chronic metabolic disorder caused by insulin resistance or inadequate insulin production, leading to severe complications such as cardiovascular diseases, kidney failure, neuropathy, and vision impairment. Factors such as sedentary lifestyles, poor dietary habits, aging, and genetic predisposition contribute to its prevalence. Early detection and timely intervention are crucial in reducing long-term health risks and improving patient outcomes. With advancements in artificial intelligence, machine learning (ML) techniques have become essential tools for analyzing complex medical data, enabling more accurate and efficient diabetes prediction than traditional diagnostic approaches. This study evaluates various supervised ML algorithms, including Logistic Regression (LR), Decision Tree (DT), Adaptive Boosting (AdaBoost), Gradient Boosting, K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB), applied to the PIMA Indian Diabetes Dataset (PIDD). The results indicate that ensemble learning models such as Random Forest and Gradient Boosting outperform traditional classifiers, achieving higher accuracy. Additionally, deep learning techniques and hybrid AI approaches show promise in improving predictive accuracy. However, challenges such as data imbalance, feature selection, and model interpretability need to be addressed for real-world implementation. Future work should focus on real-time patient monitoring, advanced deep learning models, and AI-driven diagnostic tools to enhance diabetes prediction and management.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Devi Uppati
AU  - Kamakshaiah Kolli
PY  - 2025
DA  - 2025/11/04
TI  - InSight-D Through the Lens of AI: A Comprehensive Review of Traditional Techniques and Future-Ready Frameworks
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1224
EP  - 1238
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_102
DO  - 10.2991/978-94-6463-858-5_102
ID  - Uppati2025
ER  -