Optimized Feature Selection on Breast Cancer Prevention Identifier and Classification

Considering breast cancer one of the main causes of cancer-related deaths for women worldwide is breast cancer; early and precise detection is essential. While their effectiveness, traditional diagnostic methods have drawbacks such as high costs, time duties, and the possibility of human mistake. Using the Wisconsin Breast Cancer Dataset (WBCD), this research investigates the application of machine learning (ML) and deep learning approaches for automated breast cancer classification to address these challenges. A Convolutional Neural Network modified for structured data has been examined among a number of supervised machine learning techniques, such as K-Nearest Neighbors (KNN), Random Forest , Support Vector Machine, and Logistic Regression .To improve model reliability, the study places a strong emphasis on cross-validation, hyperparameter maximizing efficiency, selection of features via connection filtering and recursive feature purification, and robust preprocessing. Based on experimental data, the CNN generated superior performance exhibiting an accuracy of 98.4% along with a ROC-AUC score of 0.995, while traditional ML models achieve competitive accuracy. The potential of incorporating ML and DL techniques into systems that support clinical decisions is shown by this comparative analysis, which may increase patient outcomes and diagnostic performance. Regarding their effectiveness, traditional diagnostic techniques including mammography, biopsy, and clinical evaluation can be costly, time taken, and susceptible to mistakes by humans. Machine learning (ML), which provides automatic, precise, and successful classification systems, has become an important instrument for assisting medical diagnosis as a result of the quick advances in computer and susceptible to mistakes by humans.