A Comprehensive Performance Analysis of Optimization-Enhanced Machine Learning Models for Breast Cancer Classification

Aryan Shrivastava, Neha Shrivas, Vivek Shukla

Breast cancer is currently one of the major causes of cancer-related deaths among women in the entire world, and early and correct diagnosis of breast cancer is vital in enhancing survival and the effectiveness of treatment. The use of machine learning (ML) methods to aid in automated classification of breast cancer has been on the rise; but traditional classifiers are often prone to shortcomings like poor feature selection and are sensitive to hyperparameter configurations, overfitting and poor generalization. In order to deal with these problems, this research conducts a detailed performance evaluation of machine learning models that are optimized to classify breast cancer. The original aim is to assess how module augmentation of current ML systems with progressive optimization can be utilized to increase predictive preciseness, dependability, and remuneration of computational energy. The researchers rely on the Wisconsin Breast Cancer Dataset, which comprises of diagnostic aspects based on digitized images of fine needles in the form of aspirate. Several supervised learning algorithms were adopted, and they consisted of Support Vector Machine, Random Forest, K-Nearest Neighbors, Logistic Regression, and Artificial Neural Networks. The given models were further optimized with the help of Genetic Algorithm, Particle Swarm Optimization, Grid Search, and Bayesian Optimization feature selection and hyperparameter tuning tools. The experimental findings indicate that the optimization method is able to enhance the classification accuracy, F1-score, and ROC-AUC and minimize the misclassification errors and increase the convergence stability. Out of the models considered, optimization-enhanced ensemble methods had the best diagnostic accuracy. The relevance of hybrid optimization-ML frameworks discussed in the findings demonstrates the significance of this type of framework in the creation of a high-quality computer-aided diagnostic system with good clinical decision support and precision oncology implications.