A novel nature inspired feature optimization algorithm to detect breast cancer using machine learning

Feature optimization (FO) is a machine learning (ML) preprocessing step that improves performance by reducing the number of features. However, FO struggles with high-dimensional medical data, leading to overfitting. In this paper, we propose Fire-Gray Optimization (FGO), a nature-inspired FO algorithm designed to obtain an optimal feature subset and overcome single FO limitations. The proposed method capitalizes on the rapid flashing behavior of fireflies and the predatory habits of gray wolves. By simulating the firefly's flashing behavior and the gray wolf's social hierarchy sequentially, this FO approach iteratively optimizes features using a dataset, providing improved and balanced exploration and exploitation capabilities for resolving challenging optimization problems. Each algorithm refines the shortcomings of others and adds a hierarchical role with enhanced global and local search capabilities. The proposed technique has been evaluated on a well-known medical dataset, the Wisconsin Breast Cancer Dataset (WBCD), which has 32 features from 569 samples. Data in WBCD is classified using six learning methods: voting classifier (soft and hard voting), decision tree, logistic regression, random forest, extreme gradient boosting, and multilayer perceptron. The experimental outcome illustrates that the proposed approach outperformed various prior works on the WBCD by achieving a detection accuracy of 98.24 % for the voting classifier with 16 features. The same optimization process was employed on another breast cancer dataset (METABRIC) to predict 5-year survival status with six similar ML algorithms. FGO-optimized data achieved 95.96 % accuracy for the voting classifier with 16 features. In summary, FGO shows promise for high-dimensional biomedical classification.