Multistage feature selection and stacked generalization model for cancer detection

Abstract

To address the issue of reliable cancer screening, this study proposes a novel approach to select key features in conjunction with a stacking classifier. It reduces the number of features required while maintaining the same diagnostic accuracy. The experimental results demonstrate that the proposed method yields superior performance in terms of accuracy, sensitivity, precision, specificity, and AUC on each benchmark dataset. This stacked model, built from Logistic Regression, Naïve Bayes, Decision Tree and a Multilayer Perceptron as meta-classifier, achieves 100% accuracy, sensitivity, specificity and AUC using the selected optimal feature subsets. The findings confirm that intelligent feature selection helps models perform better and is easier to use in identification of cancer.