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COPD is defined as a heterogeneous lung condition marked by persistent respiratory symptoms such as dyspnea, cough, and sputum production, caused by abnormalities in the airways that cause airflow obstruction.
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Related Experiment Video

Updated: Jun 24, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Construction and Validation of Active Case-Finding Tool in Community Participants with Chronic Obstructive Pulmonary

Heshen Tian^1,2, Fan Wu¹, Chuanqi Sun¹

¹State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Diseases, National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.

International Journal of Chronic Obstructive Pulmonary Disease

|June 23, 2026

Summary

This study developed a machine learning tool for early chronic obstructive pulmonary disease (COPD) detection in communities. The tool accurately identifies individuals needing further testing, improving COPD prevention strategies.

Keywords:

COPD clinical prediction model community screening machine learning

Related Experiment Videos

Last Updated: Jun 24, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Area of Science:

Pulmonary Medicine
Artificial Intelligence in Healthcare
Public Health Screening

Background:

Early diagnosis of chronic obstructive pulmonary disease (COPD) is crucial for prevention.
Traditional COPD screening tools have limitations in data accuracy and predictive power.
Active case-finding is effective but requires improved methodologies.

Purpose of the Study:

To develop an updated, convenient, and interpretable machine learning tool for COPD screening.
To enhance active case-finding strategies in community settings.
To create a tool that assists in prioritizing individuals for confirmatory spirometry.

Main Methods:

Machine learning models were developed using PyCaret and R programming language.
Data from two community-based studies in Guangdong, China, were used for training and validation.
Eleven classification models were compared, with AdaBoost showing superior performance; Shapley Additive exPlanations were used for interpretability.

Main Results:

The AdaBoost model achieved high accuracy (0.846) and AUC (0.848) for COPD prediction.
The model accurately classified COPD severity (accuracy 0.822, AUC 0.816), identifying 83% of moderate-to-severe cases.
Gradient boosting regression models effectively predicted lung function parameters (FEV1 %pred, FEV1/FVC).

Conclusions:

A machine learning-based active case-finding tool for COPD prediction and lung function assessment was developed.
The tool utilizes limited clinical data to identify high-risk individuals in community settings.
Further studies are needed to evaluate the tool's impact on referral efficiency and patient outcomes.