Study on Breast Cancer Classification Prediction based on XGBoost

Machine learning plays an important role in cancer prediction, this paper realizes the prediction of breast cancer classification by constructing the Extreme Gradient Boosting (XGBoost) algorithm, and compares and analyzes it with other algorithms, and comprehensively applies several indexes such as Accuracy, Precision, F1_Score, Hamming_Loss, etc., for the evaluation of the algorithm classification effect. The results show that the XGBoost algorithm achieves the optimal value in each index in the case of 70% and 80% training set share. XGBoost also performs well in terms of stability and predictive power. Random Forest and Multilayer Perceptron (MLP) are also reliable choices, Adaboost and Decision Tree are relatively poor in terms of comprehensive metrics and need to be chosen with caution for breast cancer classification prediction problems.