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Two New Feature Selection Methods Based on Learn-heuristic Techniques for Breast Cancer Prediction: a Comprehensive Analysis by Kamyab Karimi; Ali Ghodratnama; Reza Tavakkoli-Moghaddam is a Engineering article available to read on EtoBox.

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In recent decades, breast cancer has become one of the leading causes of mortality among women. This disease is not preventable because of its unknown causes; however, its early diagnosis increases patients' recovery chances. Machine learning (ML) can be utilized to improve treatment outcomes in healthcare operations while diminishing costs and time. In this research, we suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) and their combination with ML algorithms. This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make more precise and reliable decisions. K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), Naive Bayes, AdaBoost (AB), linear discriminant analysis (LDA), random forest (RF), logistic regression (LR), and artificial neural network (ANN) are some of the methods employed. Sensitivity, accuracy, precision, mean absolute error F-score, root mean square error, Kappa, and relative absolute error calculated the performance of the methods. This paper applied a distinctive integration of evaluation measures an

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Author

Kamyab Karimi; Ali Ghodratnama; Reza Tavakkoli-Moghaddam

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Engineering (Social Sciences)