Development and validation of an Adaboost model for breast cancer classification

The mutation of breast cells leads to breast cancer,a lump causing illnesses .A large amount of women who have the concern of cancer are suffering from mutation in their breast cells. Early diagnosis of breast cancer can significantly improve the survival rate of patients by allowing them to receive timely clinical treatment. Accurate classification from tumor data prevents patients from receiving unnecessary treatment. Therefore, correctly diagnosing breast cancer and classifying patients into malignant or benign groups is a hot topic. In this study, the Adaboost algorithm is used to sort breast cancer depending on whether it is benign or malignant by combining the one-hot coding approach and imbalance learning. The one-hot coding approach overcomes the bias caused by the LabelEncoder coding approach, and the synthetic minority over-sampling technique, which improves the classification performance of Adaboost by augmenting the categorization balanced data structure with more minority class samples, so that its diagnosis of breast cancer reaches 99% accuracy, which is better than other machine learning models, we propose this model can be used for the diagnosis of breast cancer, providing a more accurate and faster way to classify breast cancer.