What question did this study set out to answer?

This research aimed to develop interpretable machine learning models to predict obstructive airway disease risk in a non-smoking population.

March 12, 2026Open Access

Interpretable machine learning based decision tree model for predicting obstructive airway disease in a large non-smoking health screening population

Key Points

This research aimed to develop interpretable machine learning models to predict obstructive airway disease risk in a non-smoking population.
Analyzed data from 81,055 non-smoking individuals using health screening data.
Applied six machine learning algorithms including CART and Random Forest.
Developed a visualized decision tree to generate clinical decision rules.
Utilized ensemble feature aggregation to identify key predictors.
All machine learning models showed strong predictive performance.
Key predictors included age, waist-to-hip ratio, and blood pressure.
The decision tree model provided clear rules for clinical screening.

Abstract

Obstructive airway disease is defined by reduced FEV₁ and an FEV₁/FVC ratio below 70%. While pulmonary function testing is essential, few studies have used demographic, biochemical, and lifestyle data to predict disease risk in non-smoker group. This study aimed to develop interpretable machine learning (ML) models for early risk prediction and clinical screening. We analyzed data from 81,055 non-smoking individuals drawn from a health screening cohort of 549,825 participants. Six ML algorithms including CART, RF, XGBoost, LightGBM, CatBoost, and Lasso were applied to develop predictive models. All models demonstrated strong predictive performance, and an ensemble feature aggregation approach was used to identify key predictors. A CART was built with the identified features to generate a visualized decision tree, generating decision rules to support clinical screening. The key predictors included age, waist-to-hip ratio, blood pressure, and biochemical markers. This is the first large-scale ML study predicting obstructive airway disease in non-smoker group using health exam data. Interpretable models may assist early detection and clinical risk stratification.

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