DeepDiabFusion: An Interaction-Aware Neural Network Architecture for Diabetes Prediction

Mukhriddin Arabboev; Shohruh Begmatov; Saidakmal Saydiakbarov; Sukhrob Bobojanov; Khabibullo Nosirov; Jean Chamberlain Chedjou

doi:10.4108/airo.7998

Authors

Mukhriddin Arabboev Tashkent University of Information Technology
Shohruh Begmatov Tashkent University of Information Technology
Saidakmal Saydiakbarov UNICON.UZ Scientific-Engineering and Marketing Research Center
Sukhrob Bobojanov Tashkent University of Information Technology
Khabibullo Nosirov Tashkent University of Information Technology
Jean Chamberlain Chedjou University of Klagenfurt

DOI:

https://doi.org/10.4108/airo.7998

Keywords:

diabetes prediction, Artificial Neural Networks, ANN, Pima Indians Diabetes Dataset, PIDD, machine learning, accuracy

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.

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