Intelligent Diagnosis and Prediction Model of Breast Cancer Based on Stacking Ensembled Learning

Abstract:

Integrating innovative data preprocessing methods and machine learning algorithms， an intelligent prediction model is constructed based on the Breast cancer Wisconsin diagnostic dataset. Firstly， the feature recursive elimination method based on light gradient boosting machine （LightGBM） model is used for feature selection. Secondly， the integrated sampling combined with adaptive synthetic sampling （ADASYN） oversampling and one-sided selection （OSS） undersampling is used to deal with data imbalance， and a balanced training data set is obtained. Finally， with multilayer perception （MLP）， LightGBM and categorical boosting （CatBoost） as the base learner and logistic regression model as the meta-learner， an intelligent diagnosis model based on Stacking ensembled learning is constructed. It is evaluated by 5 folds cross-validation and classification prediction indicators such as accuracy， sensitivity， and area under receiver operating characteristic curve. The experimental results show that the proposed model can achieve a prediction accuracy of 98.2%， and has stable and excellent classification prediction performance， which can provide strong decision support for clinical diagnosis of breast cancer.