Construction of early warning model of lower respiratory tract infection in chemotherapy tumor patients based on SMOTE algorithm

Abstract:

Objective To construct the early warning model of lower respiratory tract (LRT) infection in chemotherapy tumor patients based on synthetic minority over-sampling technique (SMOTE) algorithm. Methods 2 384 tumor patients treated with chemotherapy in 4 tertiary hospitals in Xining City from January 2019 to June 2021 were investigated, patients were randomly divided into modeling group (n=1 668) and validation group (n=716) accor-ding to the ratio of 7:3, data of modeling group was used to construct the model, data of validation group was used to verify the constructed model, influencing factors for LRT infection were screened by univariate comparison and logistic regression analysis, the early warning model of LRT infection of chemotherapy tumor patients was constructed based on SMOTE algorithm. Results Logistic regression analysis showed that age (x₁), whether body mass index was normal (BMI, x₂), stage of malignant tumor (x₃), smoking history (x₄), combined diabetes mellitus (x₅) and combined pulmonary disease (x₆) were all risk factors for LRT infection in chemotherapy tumor patients (all P < 0.01), the original data warning model: Logit (P)=0.055x₁+0.967x₂-0.195x₃+1.383x₄+0.968x₅+0.939x₆-14.073 and early warning model based on SMOTE algorithm: Logit(P)=0.090x₁+1.092x₂-0.249x₃+1.724x₄+1.136x₅+1.344x₆-14.859 were obtained. The AUC of early warning model based on SMOTE algorithm was higher than original data warning model (0.949[95%CI: 0.937-0.961] vs 0.780[95%CI: 0.734-0.846]). Conclusion The early warning model based on SMOTE algorithm can more accurately warn LRT infection in chemotherapy tumor patients, and effectively solve the warning error caused by the imbalance of the sample data of infected and non-infected patients, the corresponding countermeasures can be selected based on the warning model.