A comparative study of metaheuristic-based machine learning classifiers using non-parametric tests for the detection of COPD severity grade

Abstract

In this paper, a comparative study between five different Machine Learning (ML) classifiers has been performed for the detection of Chronic Obstructive Pulmonary Disease (COPD) severity grade. There are various existing studies that provide various computer-aided frameworks for the diagnosis of COPD. However, such existing approaches have certain limitations such as lower performance, imbalanced data, and missing data. Also, no statistical tests were performed that could justify the validity of the proposed techniques. Hence, to provide the best model for the detection of COPD severity grade, a conceptualized framework has been provided. The performance of ML classifiers has been optimized by utilizing the Correlation-based Elephant search algorithm (CFS+ESA) and InformationGain and GainRatio feature selection techniques (FS). Experimental work carried out on the COPD patient dataset has been validated using statistical tests. The Wilcoxon test and Friedman test were utilized to compare the FS techniques and ML techniques respectively. The performance comparison has been carried out across different training-testing criteria namely, 10-fold cross-validation, 70%-30%, 75%-25%, and 80%-20%. The results from Wilcoxon tests showed that CFS+ESA has given the best results across all the training-testing criteria. Similarly, the results from Friedman’s tests demonstrated that the CFS+ESA-based Logistic Regression classifier trained using 10-fold cross-validation has outperformed the rest combinations of classifiers and training-testing criteria by achieving the highest rank.