International Journal of Diabetes & Metabolic Disorders(IJDMD)

School of Computer Science, Taylor’s University, Malaysia

Publications

• Research Article   
  Enhancing Diabetes Prediction through a Hybrid Deep Learning and Machine Learning Ensemble Using a Two-Stage Soft Voting
  Author(s): Md Ziarul Islam*, Zariya Ahmed Udaisa, Mohd Khairul Azmi Bin Hassan and Amir 'Aatieff Bin Amir Hussin
  
  Objective: This study aims to enhance the accuracy and robustness of diabetes prediction by developing a hybrid ensemble model that integrates both Deep Learning (DL) and Machine Learning (ML) classifiers through a two-stage soft voting mechanism. Research Methodology: The proposed methodology involves a comprehensive preprocessing pipeline, including label encoding for categorical features and standardization of numerical variables. Three DL architectures, Convolutional Neural Network (CNN), Feedforward Neural Network (FNN), and Ensemble Neural Networks (ENN), are independently trained alongside three ML classifiers: Logistic Regression (LR), Random Forest (RF), and XGBoost. Soft voting is applied separately within the DL and ML groups, and the resulting predictions are combined in a final hybrid soft voting ensemble. The benchmark Kaggle d.. Read More»
  

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