Abstract

In the field of medical data analysis, the major challenges are high dimensionality and complexity due to temporal behavior of medical datasets. Feature selection is crucial to overcoming these challenges since it enhances interpretability, reduces processing expenses, and boosts model performance. To analyze the medical records, it is very essential to determine the most potential features that contribute more in classification or diagnosis of disease especially in the medical in the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. This paper, highlights the importance of robust feature selection by developing a novel Hybrid feature selection framework that combines ReliefF and Ant Colony Optimization with Graph Neural Networks (ACO-GNN). The first step in the suggested approach is Ant Colony Optimization (ACO), which creates candidate feature subsets by effectively exploring the combinatorial feature space by mimicking pheromone-guided search behavior. Then, using graph-based representations of clinical variables, such as correlations between lab tests, drugs, and vital signs, Graph Neural Networks (GNNs) are used to model intricate, non-linear interactions among medical aspects. In order to ensure robustness and interpretability, ReliefF is used to rank and improve features by assessing their capacity to distinguish between patient outcome classes. The hybrid approach significantly outperforms conventional feature selection techniques like K-Nearest Neighbors with ReliefF (KNN-ReliefF) and XGBoost with SHAP Feature Importance (XGB-SHAP) in through tests on the MIMIC-III dataset, predictive performance indicators such as precision, accuracy, F1-score, recall, and AUC-ROC. The selected feature subsets offer clinically meaningful insights into critical factors influencing patient outcomes in intensive care, underscoring the potential of the ACO-GNN-ReliefF method for advancing predictive analytics and clinical decision support systems in healthcare.

Authors

K. Preethi, M. Ramakrishnan
Madurai Kamaraj University, India

Keywords

Feature Selection, Ant Colony Optimization, Graph Neural Networks, ReliefF, Medical Data Analysis, MIMIC-III, Predictive Modeling, Intensive Care, Clinical Decision Support, Machine Learning in Healthcare