A Comparative Analysis of Machine Learning Using Classification Algorithms for the Detection of Breast

Cancer deaths are one of the most important problems faced by people in developing countries. There are many ways to prevent cancer from growing in the first place, but some cancers are still incurable. Breast cancer is a serious disease that is one of the most common health problems among women and causes death worldwide. Accurate diagnosis and classification of breast cancer are important. There are many studies in the literature to predict the type of breast cancer. AI-based automation can save time and reduce errors. In this study, machine learning techniques such as logistic regression, K-nearest neighbor, decision trees, and random forests are used together with data visualization. Python was chosen as the programming language for machine learning models and visualization. The purpose of this study is to compare machine learning and data visualization tools for breast cancer detection and diagnosis. The diagnosis of these applications can be compared to the diagnosis of cancer. Data visualization and machine learning techniques can have an impact on cancer diagnosis and decision-making. While the all-featured KNN classifier model achieves the best classification (0.96%), the proposed method shows improvement in accuracy results. These results suggest that new methods can be investigated for cancer diagnosis.