<![CDATA[Early diagnosis of diabetes mellitus is crucial to prevent severe complications and reduce long-term healthcare costs, making accurate and efficient predictive models an important research focus in medical data analytics. However, one of the main challenges in diabetes prediction lies in the presence of irrelevant and redundant features within medical datasets, which can degrade classification accuracy, increase computational complexity, and reduce model generalizability. To address this issue, this study proposes a Hybrid Feature Selection (HFS) approach that integrates filter-based methods and meta-heuristic optimization to identify an optimal subset of features for diabetes prediction. In the proposed framework, statistical filter techniques combining Chi-square and Mutual Information are first employed to rank and reduce feature dimensionality by selecting the most relevant attributes. Subsequently, a Genetic Algorithm (GA) is applied to further optimize the feature subset by maximizing classification accuracy while minimizing the number of selected features. The effectiveness of the proposed HFS approach is evaluated using the Pima Indian Diabetes Dataset, consisting of 768 instances and 8 clinical features, and tested across multiple machine learning classifiers, including Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. Experimental results demonstrate that the proposed HFS significantly improves predictive performance compared to baseline models without feature selection. Specifically, the Random Forest classifier achieved the highest accuracy of 79.22%, compared to 74.03% in the baseline model, representing an improvement of approximately 5.2%. Additionally, notable improvements were observed in F1-score and AUC, with AUC increasing from 0.8336 to 0.8403. Beyond accuracy gains, the proposed method reduced feature dimensionality from 8 to 5 features, resulting in lower computational cost and faster model training time. These findings indicate that the hybrid integration of filter-based selection and meta-heuristic optimization provides a robust and efficient solution for feature selection in medical prediction tasks. Overall, the proposed HFS framework offers a promising approach for developing accurate, efficient, and reliable decision-support systems for early diabetes diagnosis.]]>
