Title: MA-TIL-GBNN: a multi-attention enhanced deep incremental learning-based diabetes prediction and drug recommendation framework

Authors: Netra S. Patil; Naveenkumar Jayakumar; Rohini B. Jadhav; Gauri R. Rao; Shashank D. Joshi; Shubhangi R. Katkar; Madhavi Mane

Addresses: Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, Maharashtra, 411 043, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India ' Department of Information Technology, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, Maharashtra, 411 043, India ' Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, Maharashtra, 411 043, India ' Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, Maharashtra, 411 043, India ' Panchakarma, Bharati Vidyapeeth (Deemed to be University) College of Ayurved, Pune, Maharashtra, 411 043, India ' Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, Maharashtra, 411 043, India

Abstract: This research is designed to develop an advanced framework for the prediction of diabetes and the personalised recommendation of drugs. This research proposes a multi-attention enhanced task incremental learning coupled gradient boost deep neural network (MA-TIL-GBNN) approach aimed at the prediction of diabetes and recommendation of drugs based on their types. The approach integrates MA techniques into the DNN and generative adversarial network-based data augmentation (GDA) to effectively analyse the health indicators. Additionally, the TIL enhances the training of data, and the Light GBM facilitates efficient processing. The experimental results using the Diabetes Health Indicators Dataset showed 97.79% accuracy, 98.22% sensitivity, and 97.36% specificity, respectively.

Keywords: diabetes prediction; drug recommendation; deep learning; machine learning; healthcare management.

DOI: 10.1504/IJBET.2026.154176

International Journal of Biomedical Engineering and Technology, 2026 Vol.50 No.4, pp.374 - 409

Received: 13 Sep 2025
Accepted: 15 Nov 2025
Published online: 15 Jun 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article