AI-Enabled Breast Cancer Diagnosis for Health Networks Using Salp Swarm and Sparrow Search Optimized Adaboost

Breast cancer is one of the most common and deadly diseases worldwide, making early and accurate detection critical for improving patient outcomes. Machine learning has shown significant promise in automating breast cancer diagnosis, but selecting the right features and optimizing model performance remain challenging. In this context, this work provides an AI-enabled method employing an AdaBoost model optimized using Salp Swarm Optimization (SSO) for feature selection and Sparrow Search Algorithm (SSA) for hyperparameter tuning for breast cancer detection. Our proposed model attained an accuracy of $\mathbf{9 9. 1 2 \%}$, precision ($\mathbf{9 9. 1 3 \%}$), recall ($\mathbf{9 9. 1 2 \%}$), and F1-score $(\mathbf{9 9. 1 2 \%})$. The findings show that the suggested method offers a dependable and efficient way to identify breast cancer, therefore greatly raising the diagnostic accuracy in medical systems.