A novel machine learning based hybrid approach for breast cancer relapse prediction

Abstract

The second leading cause of death for women is breast cancer, which is growing. Some cancer cells may remain in the body, so relapse is possible even if treatment begins soon after diagnosis. Since there are now many machine learning (ML) approaches to recurrence prediction in breast cancer, it is important to compare and contrast them to find the most effective one. Datasets with many features often lead to incorrect predictions because of this. In this study, correlation-based feature selection (CFS) and the flower pollination algorithm (FPA) are used to improve the quality of the wisconsin prognostic breast cancer (WPBC) and University Medical Centre, Institute of Oncology (UMCIO) breast cancer relapse datasets respectively. Data imputation, scaling, pre-process raw data. The second stage uses CFS to select discriminative features based on important feature correlations. The FPA chose the optimum attribute combination for the most precise answer. We tested the approach using 10-fold cross-validation stratification. Various trials show 84.85% and 83.92% accuracy on the WPBC and UMCIO breast cancer relapse datasets, respectively. The hybrid method performed well in feature selection, increasing the accuracy of the relapse classification for breast cancer.