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

Abstract

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.

Introduction

This study investigates the use of machine learning (ML) and optimization techniques to improve breast cancer classification using the Wisconsin Breast Cancer Dataset. Breast cancer remains one of the leading causes of cancer-related deaths among women, making early and accurate diagnosis essential. While conventional diagnostic methods such as mammography and biopsy are effective, they are limited by inter-observer variability, high-dimensional data, and difficulty in identifying subtle early-stage abnormalities. Machine learning offers a promising solution by providing automated, consistent, and accurate diagnostic support.

The study evaluates five supervised ML algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Logistic Regression (LR). To enhance model performance, four optimization methods—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bayesian Optimization, and Grid/Random Search—are applied for feature selection and hyperparameter tuning. A hybrid framework is proposed in which optimization techniques first identify the most relevant features and then optimize model parameters before classification.

The dataset contains 569 samples with 30 numerical features, including measurements such as radius, texture, perimeter, and concavity. After preprocessing through feature standardization, the data is split into 80% training and 20% testing, with 10-fold cross-validation used to improve model reliability.

Experimental results demonstrate that optimization significantly improves classification performance. Among the baseline models, Random Forest achieved the highest accuracy of 95.10%. After optimization, PSO-optimized Random Forest (PSO-RF) produced the best results with 98.05% accuracy, 0.98 precision, 0.97 recall, 0.97 F1-score, and 0.99 ROC-AUC. Although optimization increased training time, the improvement in diagnostic accuracy and reduction in misclassification justified the additional computational cost. The confusion matrix showed only 11 misclassified cases, including just 4 false negatives, which is particularly important for reducing missed cancer diagnoses.

The experimental implementation was carried out using Python, Scikit-learn, and TensorFlow on a workstation equipped with an Intel Core i7 processor, 16 GB RAM, and an NVIDIA GTX 1660 GPU. Standard parameter settings were used for GA, PSO, and Bayesian Optimization based on recent healthcare ML studies.

Performance analysis confirmed that optimization improved all evaluated models by approximately 2–3% across major metrics. PSO demonstrated faster convergence than GA, while Bayesian Optimization provided efficient hyperparameter tuning and improved neural network generalization. Statistical analysis using a paired t-test (p < 0.05) indicated that these improvements were significant.

Feature importance analysis identified concave points, radius mean, and perimeter mean as the most influential predictors of breast cancer, consistent with previous medical AI research. Overall, the study concludes that integrating optimization algorithms with machine learning substantially enhances breast cancer diagnosis, producing more accurate, reliable, and clinically applicable predictive models. The findings suggest that hybrid optimization-based ML frameworks have strong potential to support future computer-aided diagnostic systems in healthcare.

Conclusion

This paper has given a thorough performance analysis of machine learning models with optimization to classify breast cancer. These results indicate that metaheuristic and probabilistic optimization strategy integration can yield much better predictive accuracy, recall, F1-score and ROC-AUC than traditional baseline classifiers. Dimensional redundancy was minimized by means of a feature selection process that is based on optimization, and model convergence and generalizability were increased in an attempt to optimize hyperparameters systematically. These findings are aligned with the current studies in the field of healthcare AI that highlight the importance of optimization methods in enhancing diagnostic reliability and strength (Abd Elaziz et al., 2022; Taye, 2023). Among the tested models, the PSO-optimized Random Forest was the most successful model, which has the highest accuracy over all and ROC-AUC and stable convergence behavior. Swarm-based optimization was also useful in the area of parameter selection and detection of malignant cases, which is consistent with the recent comparative biomedical optimization research (Hussain et al., 2022; Kumar et al., 2024). Bayesian-optimized ANN was also found to be highly generalized and computationally efficient, which also underlines the possibility of probabilistic hyperparameter search methods in medical practice (Snoek et al., 2022). The value of the study is that it offers a comparative framework that is able to compare various optimization methods on a variety of classifiers under the same evaluation conditions. In terms of healthcare AI, the research contributes to the creation of high-quality computer-aided diagnostic applications that can help clinicians to identify breast cancer at an early stage. In practice, optimization-optimal ML models have the potential to help minimize the occurrence of diagnostic errors, enhance the efficiency of screening, and promote precision oncology efforts, which enhances the current trend of artificial intelligence integration into clinical decision support systems.

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Copyright

Copyright © 2026 Aryan Shrivastava, Neha Shrivas, Vivek Shukla. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.