<![CDATA[Statistics show that among the 1.67 million cancer reported cases worldwide, breast cancer is the most common cancer among women and constitutes the largest burden of the disease in developing countries. However, if detected early enough, it can be managed. Mammography is one of the best ways to identify and diagnose breast abnormalities among various medical imaging modalities. It typically detects signs and symptoms of breast cancer, including microcalcifications, lumps, nodules, architectural abnormalities, asymmetry, bilateral asymmetry, etc. These features can be benign or cancerous when they appear in the breast. Researchers have focused on creating fully automated computer-aided design methods to help radiologists combat this type of cancer. Artificial Intelligence( AI) -based algorithms have been essential in creating systems that allow for automated diagnosis, rapid response, and low mortality. In this work, several machine learning methods were compared—such as logistic regression, naive Bayesian Gaussian algorithms, support vector machines (SVM), linear support vector machines (SVM), and artificial neural networks (ANN). Processing time and accuracy were the main evaluation metrics where naive Bayes outperformed SVM, followed by linear SVM and logistic regression, with ANNs failing in accuracy. These results highlight how naive Bayes algorithms can help in early detection of breast cancer, leading to faster and more efficient treatments and ultimately better patient care.]]>
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