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Explainable Machine Learning Models for Predicting FEV₁ in Non-Smoking Taiwanese Men Aged 45-55 Years.

Chih-Yueh Chang^1,2,3, Dee Pei⁴, Yen-Liang Kuo^1,2

¹Division of Chest Medicine, Department of Internal Medicine, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 243089, Taiwan.

Diagnostics (Basel, Switzerland)

|December 30, 2025

Summary

This summary is machine-generated.

Machine learning models slightly improved prediction accuracy for forced expiratory volume in one second (FEV1) compared to traditional regression. Key factors influencing FEV1 include lactate dehydrogenase, body weight, and physical activity.

Keywords:

FEV1 SHAP health examination cohort lactate dehydrogenase lung function machine learning

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Area of Science:

Pulmonary Medicine
Biostatistics
Data Science

Background:

Traditional regression models inadequately explain variations in forced expiratory volume in one second (FEV1).
Machine learning (ML) offers potential to identify nonlinear patterns in FEV1 prediction.
Non-smoking Taiwanese men aged 45-55 were analyzed.

Purpose of the Study:

Compare the predictive performance of ML models (Random Forest, Stochastic Gradient Boosting, XGBoost) against multiple linear regression (MLR) for FEV1.
Identify key predictors of FEV1 using ML and interpret their effects.
Evaluate the utility of ML in understanding FEV1 determinants.

Main Methods:

Analysis of 23,943 non-smoking Taiwanese men aged 45-55 from the MJ Health Screening Cohort.
Comparison of Random Forest, Stochastic Gradient Boosting, and XGBoost against Multiple Linear Regression using repeated train-test splits.
Model performance assessed using RMSE, RAE, RRSE, and SMAPE; variable importance interpreted with Shapley Additive Explanations (SHAP).

Main Results:

ML models demonstrated slightly lower prediction errors than MLR.
Top predictors for FEV1 included lactate dehydrogenase (LDH), body weight (BW), education level, leukocyte count, total bilirubin, and sport area.
SHAP analysis revealed negative associations for LDH and leukocyte count, and positive associations for BW, bilirubin, education, and physical activity.

Conclusions:

ML approaches offer modest accuracy improvements and enhanced interpretability over MLR for FEV1 prediction.
Biochemical (e.g., LDH, bilirubin) and lifestyle factors (e.g., BW, education, physical activity, inflammation markers) significantly contribute to FEV1 in healthy middle-aged men.