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January 10, 2026International Journal of COPD0 citationsOpen Access

An Interpretable AdaBoost Model for 1-Year Readmission Risk Prediction in AECOPD Patients with Hypertension

XZXinxin ZhangFirst Affiliated Hospital of Shihezi University Medical College MHMaolang HeShihezi University JZJingyi ZhangShihezi University

Key Points

The aim is to predict 1-year readmission risk for patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) who also have hypertension.
Developed an interpretable AdaBoost predictive model.

Structured PICO

Does an AdaBoost machine learning model accurately predict 1-year readmission risk in patients with AECOPD complicated by hypertension?

Population

2,042 patients aged ≥ 40 years with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) complicated by hypertension, mean age 76.23, 54.11% male, from a single center in China.

Intervention

AdaBoost machine learning model for predicting 1-year readmission risk

Comparator

Other machine learning models (Decision Tree, K-nearest neighbors, Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, XGBoost)

Outcome

1-year readmission

Abstract

This study developed an interpretable AdaBoost-based online calculator for 1-year readmission risk assessment in AECOPD patients by hypertension. The tool highlight the importance of addressing hypercoagulability and nutritional status to reduce readmission risk. Further external multi-center validation is needed to enhance its generalizability.

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