Machine learning model development and validation using SHAP: predicting 28-day mortality risk in pulmonary fibrosis patients.

Abstract

Background: Early prediction of mortality risk within 28 days of admission is crucial for personalized treatment in patients with pulmonary fibrosis (PF). This study aims to develop a predictive model for 28-day mortality risk in PF patients using interpretable machine learning (ML) methods.

Methods: Data from patients with pulmonary fibrosis were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The study endpoint was mortality within 28 days of admission. Feature selection was performed using logistic regression and LASSO algorithms. Six machine learning algorithms-decision tree, k-nearest neighbors (KNN), LightGBM, single-hidden-layer neural network, support vector machine (SVM), and extreme gradient boosting (XGBoost)-were employed to construct risk prediction models. Additionally, SHapley Additive exPlanations (SHAP) were utilized to interpret the predictive models.

Results: Among the six evaluated machine learning models, the LightGBM model demonstrated robust predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.819. SHAP analysis revealed that length of ICU stay, respiratory rate, and white blood cell count were the three most important features for predicting 28-day mortality risk in PF patients, with ICU stay duration having the most significant impact.

Conclusion: This study indicates that machine learning methods hold potential for early prediction of mortality risk within 28 days of admission in patients with pulmonary fibrosis. Moreover, SHAP analysis enhanced the interpretability of the LightGBM model, thereby guiding clinical decision-making.