Title: An ensemble framework-stacking and feature selection technique for detection of breast cancer

Authors: Vikas Chaurasia; Saurabh Pal

Addresses: Department of Computer Applications, VBS Purvanchal University, Jaunpur, UP, India ' Department of Computer Applications, VBS Purvanchal University, Jaunpur, UP, India

Abstract: Breast cancer is the second most common cancer in women worldwide. The machine learning (ML) method is a modern and accurate technique that researchers have recently applied to predict and diagnose breast cancer. In this research article, we developed stack-based ensemble techniques and feature selection methods for the comprehensive performance of the algorithm and comparative analysis of breast cancer datasets with reduced attributes and all attributes. This article uses five-feature selection technique because it affects the overall performance of the model. After applying feature selection method, now we have a dataset with reduced features as well as all features. We implemented logistic regression on a dataset with all features and a dataset with reduced features. Finally, we see that the dataset with reduced features have got improved accuracy.

Keywords: breast cancer; k-nearest neighbour; KNN; perceptron; stacking; machine learning; feature selection; algorithm; ensemble techniques; logistic regression; sub-models.

DOI: 10.1504/IJMEI.2022.122283

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.3, pp.240 - 251

Received: 30 May 2020
Accepted: 19 Jul 2020
Published online: 19 Apr 2022 *

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