Exploring Advanced Optimization Techniques in Breast Cancer Prediction

Abstract

This paper highlights the pressing global need for early and accurate breast cancer prediction. It discusses the potential of optimization algorithms, specifically Genetic Algorithm (GA), Maxima and Minima (MM), and Simulated Annealing (SA), to improve breast cancer prediction. GA notably outperforms MM and SA in accuracy, precision, recall, and F1-score. This emphasizes the value of optimization algorithms in refining breast cancer prediction models. The paper stresses the importance of accurate prediction in reducing breast cancer prognosis and mortality rates.  It acknowledges the limitations of conventional methods, including subjectivity and the risk of false negatives. Optimization algorithms, however, are praised for their ability to handle extensive datasets, discover subtle trends, and adapt to evolving data, positioning them as indispensable tools in improving breast cancer prediction. Furthermore, the paper suggests that optimization algorithms can integrate diverse data sources, such as genomics, imaging records, and medical histories, offering a comprehensive patient risk assessment.  In conclusion, this research underscores the global importance of early and precise breast cancer prediction and advocates for continued exploration of optimization algorithms, process learning, and artificial intelligence to enhance prediction accuracy and effectiveness.