Optimized Breast Cancer Detection Using Genetic Algorithm and Ensemble Machine Learning Models

Abstract

Breast cancer detection is a critical area in medical diagnostics, and accurate classification of breast cancer can significantly enhance early diagnosis and treatment. This study examines the application of Genetic Algorithm (GA) for feature selection in conjunction with various ensemble machine learning classifiers to enhance the effectiveness of breast cancer detection models. In this study genetic algorithm is utilized for feature selection from Cancer dataset, the purpose of GA is reduced the dataset dimensionality, Also in this research the different machine learning algorithms are used like Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), and Stochastic Gradient Descent (SGD. Further in this study to enhance prediction accuracy utilized the ensemble learning methods such as Extra Tree model ,Bagging and AdaBoost. The proposed method used utmost features, which were used in various machine learning and ensemble learning algorithms to evaluate their performance. Here the performance metrics like accuracy, precision, recall F1-score and AUC (area under curve). The results shows that the Extra Tree method achieve highest accuracy 99.42% the other algorithms like SVM and Random Forest also perform good with accuracy 98%.