关键词: 集成学习, 逐步回归, 特征筛选, 疾病预测

Abstract: Machine learning and deep learning techniques can be used to solve many problems in me- dical classification prediction. Among them, some have higher prediction accuracy, but the others have limited accuracy. This paper proposes an ensemble learning algorithm based on C-AdaBoost model to predict breast cancer diseases. Stepwise regression is used to re-select existing features. The C-AdaBoost model is combined to make the prediction better. A large number of experiments show that 1) the optimal combination of features, that determines whether breast cancer recurs and whether breast cancer is benign, are found, and 2) the proposed ensemble learning algorithm based on C-AdaBoost improves the prediction accuracy by at most 19.5% in comparison to the machine learning classifiers such as SVM, Naive Bayes, RandomForest and traditional ensemble learning models, which can better help doctors make clinical decisions.

Key words: ensemble learning, stepwise regression, feature selection, disease prediction