D-HGFS: A Hybrid Greedy Forward Selection Framework for Robust Diabetes Prediction on Imbalanced Datasets

Diabetes-prediction models based on machine learning (ML) are often affected by inconsistent feature importance rankings. Different feature-selection algorithms emphasize different data characteristics and produce divergent ranked feature sets. Relying on a single ranking method can therefore lead to biased, unstable feature selection. To address this limitation, this work proposes Diabetes Hybrid Greedy Forward Selection (D-HGFS), a hybrid ML three-layer diabetes-prediction framework that integrates multi-perspective feature ranking with robust imbalance handling and classification. In the first layer, a hybrid model of ADASYN and the Tomek-links approach is utilized to tackle the issue of imbalanced classes. Subsequently, the second layer consists of seven heterogeneous feature-ranking algorithms from four categories: domain knowledge, filter, wrapper, and XAI-based methods. These methods are applied in parallel for fast execution. The D-HGFS algorithm aggregates these rankings to select the most informative features. This algorithm attempts to reduce the number of utilized features in one hand and enhance the prediction accuracy in the other hand. D-HGFS employs seven heterogeneous feature-ranking methods followed by a nested greedy feature-selection procedure. A leakage-free repeated nested cross-validation framework with five outer folds repeated 10 times was used, resulting in 50 outer-test evaluations. All preprocessing, imbalance handling, classifier selection, feature ranking, and feature selection were restricted to the corresponding training partitions. The final performance of the proposed framework was then exclusively evaluated on untouched outer-test folds. Finally, the third layer consists of a binary XGBoost classifier. Experimental results show that the proposed framework outperforms baseline XGBoost models while reducing the feature set by 42%. These findings demonstrate that combining multiple feature-ranking perspectives with effective imbalance handling produces a robust, accurate, and clinically efficient diabetes-prediction model.