DeepClassifier: A Data Sampling-Based Hybrid BiLSTM-BiGRU Neural Network for Enhanced Type 2 Diabetes Prediction

Abdullahi Abubakar Imam^1,*, Sahalu Balarabe Junaidu², Hussaini Mamman³, Ganesh Kumar³, Abdullateef Oluwagbemiga Balogun³, Sunder Ali Khowaja⁴, Shuib Basri³, Luiz Fernando Capretz⁵, Asmah Husaini⁶, Hanif Abdul Rahman⁶, Usman Ali¹, Fatoumatta Conteh¹
1 School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, ABS Building, Bandar Seri Begawan, Brunei Darussalam
2 Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria
3 Department of Computing, Universiti Teknologi PETRONAS, Sri Iskandar, Malaysia
4 School of Computer Science, Faculty of Computing, Digital and Data, Technological University (TU), Dublin, Ireland
5 Department of Electrical and Computer Engineering, Western University, London, ON, Canada
6 PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, Bandar Seri Begawan, Brunei Darussalam
* Corresponding Author: Abdullahi Abubakar Imam. Email: email
(This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.076187

Received 15 November 2025; Accepted 21 January 2026; Published online 06 March 2026

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Abstract

Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge. For predicting diabetes, this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory (BiLSTM) network with a bidirectional gated recurrent unit (BiGRU). A robust DL model, developed by combining various datasets with weighted coefficients, dense operations in the connection of deep layers, and the output aggregation using batch normalization and dropout functions to avoid overfitting. The goal of this hybrid model is better generalization and consistency among various datasets, which facilitates the effective management and early intervention. The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks. The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.