Feature Selection Using a Hybrid Approach Depends on Filter and Wrapper Methods for Accurate Breast Cancer Diagnosis

Abstract

Breast cancer is the biggest cause of mortality in women, outscoring all other malignancies. Diagnosing breast cancer is hard because the disease is complicated, treatment methods change, and there are many different kinds of patients. Information technology and artificial intelligence contribute to improve diagnostic procedures, which are critical for care and treatment as well as reducing and controlling cancer recurrence. The primary part of this research is to develop a new feature selection strategy based on a hybrid approach that combines two methods for selecting features: the filter and the wrapper. In two stages, this method reduces the number of features from 30 to 15 to increase and improve classification accuracy. The suggested method was tested using the Wisconsin Breast Cancer Dataset dataset (WDBC). To enhance the classification of breast cancer tumors, a soft voting classifier was used in this study. The proposed methodology outperforms previous research, achieving 1 for the F1 score, 1 for AUC, 1 for recall, 1 for precision, and 100% for accuracy. Furthermore, 10-fold cross-validation has a 98.2% accuracy rate.