A method for diagnosis of pneumonia infection based on improved Boosting ensemble model

YANG Qian; WANG Li; ZHANG Ping; GONG Yanyan; FU Yuye

doi:10.12122/j.issn.1674-4500.2025.04.07

Abstract：Objective To propose a diagnosis method for pneumonia infection based on improved Boosting integration model.Methods A total of 315 patients with pneumonia infection who were examined by CT in Shaanxi Provincial People's Hospital from September 2023 to May 2024 were selected,and CT diagnosis was carried out for all patients.In the preprocessing stage of CT images,image enhancement technology was applied to improve the image quality and ensure that the model acquired clearer image information during feature extraction.In the feature extraction process,texture features,shape features and pixel intensity information are extracted through the XGBoost framework,and the principal component analysis is used to reduce the feature dimensions.In addition,the sample imbalance problem is solved by introducing a focus loss function to ensure that the model has a more balanced focus on benign and malignant samples.Meanwhile,Bayesian optimisation is used in the hyperparameter optimisation process to construct a Gaussian process regression model to adjust the hyperparameters,thus ensuring that the optimal parameter combinations are selected to further improve the prediction accuracy of the model.Results The diagnostic method proposed in this study has a mean area under the curve(mAUC)value of 0.9649 and an F1 score of 0.9423 in the test set,which significantly outperforms the comparative models such as lightweight gradient booster,random forest,and K-nearest neighbour.Conclusion The diagnostic method proposed in this study provides an effective tool to improve the identification and early intervention of pneumonia infections,helping physicians to identify high-risk patients earlier and develop personalised treatment plans.