TY  - EJOU
AU  - Imam, Abdullahi Abubakar 
AU  - Junaidu, Sahalu Balarabe 
AU  - Mamman, Hussaini 
AU  - Kumar, Ganesh 
AU  - Balogun, Abdullateef Oluwagbemiga 
AU  - Khowaja, Sunder Ali 
AU  - Basri, Shuib 
AU  - Capretz, Luiz Fernando 
AU  - Husaini, Asmah 
AU  - Rahman, Hanif Abdul 
AU  - Ali, Usman 
AU  - Conteh, Fatoumatta 

TI  - DeepClassifier: A Data Sampling-Based Hybrid BiLSTM-BiGRU Neural Network for Enhanced Type 2 Diabetes Prediction
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 3
SN  - 1526-1506

AB  - 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.
KW  - Diabetes; deep learning; prediction; BiLSTM; BiGRU; classification; data sampling

DO  - 10.32604/cmes.2026.076187