Enhanced Breast Cancer Risk Classification Through Genetic Algorithm-Based Feature Selection and Machine Learning Techniques

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Abstract
Breast cancer remains one of the leading causes of mortality among women worldwide and represents a major global health challenge. Accurate classification of breast tumors as benign or malignant is therefore of critical importance for timely diagnosis and effective treatment. This study aims to enhance breast cancer risk classification by integrating machine learning (ML) techniques with a genetic algorithm-based feature selection method. Initially, multiple ML algorithms are applied to features extracted from digitized images obtained through fine-needle aspiration (FNA) of breast masses. Subsequently, a genetic algorithm-based feature selection approach is employed to identify a subset of the most discriminative features. The results demonstrate that ML models utilizing the feature subsets selected by the genetic algorithm consistently achieve higher classification accuracy compared to their baseline counterparts. This highlights the effectiveness of the proposed feature selection strategy in improving the discriminative capacity of ML models. Beyond the observed improvements in accuracy, the refined ML models developed in this study show potential for more precise and reliable breast cancer diagnoses. By enhancing the performance of ML-based decision support systems, the genetic algorithm-based feature selection approach may contribute to the advancement of personalized treatment strategies in breast cancer care.

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