Research Article
DeepDiabFusion: An Interaction-Aware Neural Network Architecture for Diabetes Prediction
@ARTICLE{10.4108/airo.7998, author={Mukhriddin Arabboev and Shohruh Begmatov and Saidakmal Saydiakbarov and Sukhrob Bobojanov and Khabibullo Nosirov and Jean Chamberlain Chedjou}, title={DeepDiabFusion: An Interaction-Aware Neural Network Architecture for Diabetes Prediction}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={4}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2025}, month={6}, keywords={diabetes prediction, Artificial Neural Networks, ANN, Pima Indians Diabetes Dataset, PIDD, machine learning, accuracy}, doi={10.4108/airo.7998} }- Mukhriddin Arabboev
Shohruh Begmatov
Saidakmal Saydiakbarov
Sukhrob Bobojanov
Khabibullo Nosirov
Jean Chamberlain Chedjou
Year: 2025
DeepDiabFusion: An Interaction-Aware Neural Network Architecture for Diabetes Prediction
AIRO
EAI
DOI: 10.4108/airo.7998
Abstract
Accurate prediction of diabetes onset is essential for effective early diagnosis and clinical intervention. This study presents a performance analysis of several machine learning (ML) algorithms applied to the Pima Indians Diabetes Dataset (PIDD), with a primary focus on a novel Artificial Neural Network (ANN) architecture, referred to as DeepDiabFusion. The proposed model integrates feature-wise normalization, parallel dense sublayers, and an interaction-aware fusion mechanism to capture complex feature relationships often overlooked by conventional models. Comparative experiments were conducted against seven traditional ML algorithms, including Logistic Regression, Random Forest, and Gradient Boosting, as well as state-of-the-art ANN-based models from recent literature. Performance was evaluated using accuracy, precision, recall, and area under the curve (AUC) metrics. The proposed model achieved an accuracy of 93.04%, precision of 86.21%, recall of 93.10%, and AUC of 0.951—outperforming all baseline and previously reported models. These results demonstrate the superior classification performance and practical applicability of the proposed ANN framework in clinical decision support systems for early diabetes detection and management.
Copyright © 2025 M. Arabboev et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
