Advanced Hybrid Feature Selection Using Harvest Algorithm, Convolutional Neural Networks and CFS

Abstract

Predictive modeling and data analysis are severely hampered by the enormous dimensionality and complexity of medical datasets. Enhancing model performance, interpretability, and computing efficiency all depend on careful feature selection. In order to extract the most pertinent characteristics from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, this paper offers a novel hybrid feature selection method that combines the Harvest Algorithm, Convolutional Neural Networks (CNN), and Correlation-Based Feature Selection (CFS). De-identified medical records, including diagnostic codes, vital signs, prescriptions, and other clinical observations, are all included in the MIMIC-III dataset. Our suggested approach makes use of CNN for deep feature extraction, the Harvest Algorithm for the first feature subset creation, and CFS for the final feature selection based on correlation measures. Test findings show that when compared to conventional feature selection techniques Random Forest with Information Gain Method (RF-IG) and SVM with Recursive Feature Elimination (SVM-RFE), our hybrid strategy greatly increases the accuracy and efficiency of prediction models. The chosen characteristics demonstrate the potential of our approach in clinical decision support and medical data analysis by offering significant insights into important variables influencing patient outcomes.