ML based with Decision Tree Method for Classifying The Breast Cancer Level

Abstract

BC's high mortality and morbidity rates endanger female patients. Thus, a breast cancer detection method is essential. Logistic regression, DTs, random forests, and CNN predicted breast cancer. Predicting early breast cancer symptoms requires ML. This study uses three classification ML techniques. We'll evaluate each algorithm's performance and accuracy. Classification systems must carefully manage and preprocess unbalanced data. We'll train ML models on BC patient data. Performance and accuracy comparisons identify the best algorithm for this task. This study will compare BC classification models to determine the optimal approach. This study predicts BC classification system accuracy.