A Robust Machine Learning Model for Breast Cancer Prediction
Rachna, Chahil Choudhary, Jatin Thakur · 2024
Breast cancer is a common form of cancer that can afflict people of any gender and develops when cells in the breast tissue grow malignantly. Breast cancer symptoms might include a lump in the breast and modifications to the breast's size, shape, or texture. Age, family history, and genetic abnormalities are a few things that might make someone more likely to have breast cancer. Treatment options for breast cancer can vary depending on the stage and severity of cancer, but common approaches include surgery, radiation therapy, chemotherapy, and hormone therapy. Early detection through regular screenings, such as mammograms, can improve treatment outcomes. A recommended model utilizes a combination of feature selection, feature extraction, and ensemble learning techniques to effectively analyze patient information and make accurate predictions regarding breast cancer. Although feature extraction tries to minimize the dimensionality of the data and enhance model performance, feature selection is used to pinpoint the most pertinent aspects for the model to concentrate on. The precision of the model's predictions is also increased by using ensemble learning strategies like bagging. Classification algorithms like random forest and support vector machines are used to discover essential traits suggestive of breast cancer and achieve high predictive accuracy. Feature selection is critical to the model's success, as it helps to eliminate irrelevant or redundant features and enhances the model's ability to identify basic patterns in the data.