Comparative Analysis of Meta-heuristic Feature Selection and Feature Extraction Approaches for Enhanced Chronic Kidney Disease Prediction

Chronic Kidney Disease (CKD) has garnered significant attention over the past decades, primarily due to its lack of symptoms in the early stages. The objective of this research paper is to evaluate and contrast the effects of various feature extraction methods, such as Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Independent Component Analysis (ICA), and meta-heuristic feature selection methods like Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) for prediction. The classification models employed for evaluating the selected features include Artificial Neural Network (ANN), Random Forest Classifier (RF), Multilayer Perceptron Classifier (MLP), and K-Nearest Neighbors (KNN). The issue of overfitting and underfitting has been addressed. The results are computed based on accuracy for both the training and testing sets and AUC-ROC scores, which have been visualized. We found out that the meta-heuristic optimization feature selection algorithms improve the performance of the models by approximately 19% compared to feature extraction techniques.