Construction and validation of a machine learning-based model for predicting pneumonia risk in patients with hemorrhagic stroke
Abstract
Objective This study aimed to develop and validate a distinct, stable, and interpretable predictive model using machine learning techniques to identify individuals at high risk of pneumonia early after admission. The goal was to provide a potential quantitative reference for implementing preventive interventions in clinical practice. Methods A retrospective nested case–control design was adopted. A total of 822 patients with hemorrhagic stroke admitted between January 2019 and October 2024 were enrolled. Feature selection was performed using LASSO regression to eliminate multicollinearity and identify key predictors. Five machine learning algorithms—logistic regression (LRC), gradient boosting classifier (GBC), random forest classifier (RFC), multilayer perceptron classifier (MLPC), and support vector machine classifier (SVC)—were employed to construct predictive models. Hyperparameters were optimized through 10-fold cross-validation and grid search. Model performance was comprehensively evaluated on an independent test set using metrics including area under the curve (AUC), accuracy, sensitivity, precision, and F1-score. Finally, SHAP (SHapley Additive exPlanations) values were applied to interpret the optimal model and elucidate the contribution of each feature to the prediction. Results LASSO regression selected 14 key predictors from 57 initial variables. Among the five models, the logistic regression model achieved the best performance on the test set. SHAP-based…
Read the paper
Related studies
- Severe clinical outcomes of COVID-19 associated with proton pump inhibitors: a nationwide cohort study with propensity score matching
- Levofloxacin-Induced Tendon Rupture: A Case Report and Review of the Literature
- Association between cord blood 25-hydroxyvitamin D concentrations and respiratory tract infections in the first 6 months of age in a Korean population: a birth cohort study (COCOA)
- Neuroprotective Effects of Epicatechin against Oxidative Stress-Induced Cognitive Impairment: A Systematic Review and Meta-Analysis
- Quality evaluation and in vitro interaction studies between levofloxacin 250mg and diclofenac sodium 50mg tablets
- Molecular Epidemiology of mcr-1 -Positive Polymyxin B-Resistant Escherichia coli Producing Extended-Spectrum β-Lactamase (ESBL) in a Tertiary Hospital in Shandong, China
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.