Enhanced Breast Cancer Diagnosis Through an Integration of MSVM and Genetic Algorithms

Breast cancer remains a major health concern among women, often spreading from the breast to other parts of the body, and is the second leading cause of death in women. While early detection significantly increases the chances of successful treatment, current methods face challenges in accuracy and efficiency. This paper presents an approach for early-stage breast cancer detection using a combination of genetic algorithms and decision trees. The genetic algorithm, an iterative technique, enhances the speed of results, while the decision tree aids in classification. Additionally, a multi-support vector machine (MSVM) is employed for feature extraction and comparison with trained images. The proposed model is evaluated based on classification accuracy, precision, F-score, and recall. Simulation results demonstrate improved performance over existing systems