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Updated: Nov 26, 2025
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
The role of artificial intelligence in identifying asthma in pediatric inpatient setting
Gang Yu1, Zheming Li1, Shuxian Li2
1Department of IT Center, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Insights
An artificial intelligence (AI) model accurately identifies childhood asthma, aiding pediatricians in correct diagnoses and reducing misdiagnosis of respiratory illnesses. This improves asthma control and limits unnecessary antibiotic and steroid use.
Area of Science:
- Pediatric Pulmonology
- Medical Informatics
- Artificial Intelligence in Healthcare
Background:
- Childhood asthma incidence is rising in China due to misdiagnosis as common respiratory infections.
- Misdiagnosis leads to overuse of antibiotics and systemic glucocorticoids.
- Delayed asthma diagnosis can cause chronic airway inflammation, impacting athletic ability and leading to adult chronic obstructive pulmonary disease (COPD).
Purpose of the Study:
- To develop and evaluate machine learning models for accurate asthma identification in children.
- To assist primary pediatricians in distinguishing asthma from other respiratory conditions.
Main Methods:
- Machine learning models (CatBoost, Logistic Regression, Naïve Bayes, SVM) were trained on electronic medical records (EMRs).
- Models were evaluated using two independent test sets from pediatric pulmonology and non-pulmonology departments.
- Performance was assessed using accuracy and area under the curve (AUC).
Main Results:
- The CatBoost model demonstrated superior performance on both test sets.
- On TestSet-1 (Pulmonology), CatBoost achieved 84.7% accuracy and 90.9% AUC.
- On TestSet-2 (Non-Pulmonology), CatBoost achieved 96.7% accuracy and 98.1% AUC.
Conclusions:
- An AI model can rapidly and accurately detect childhood asthma in general pediatric wards.
- This AI tool can support primary pediatricians in making correct asthma diagnoses.
- The AI model has significant clinical value for improving asthma control, optimizing resources, and reducing medication abuse.
Background:
The incidence of asthma in Chinese children has rapidly increased as a result of inadequate management. This is mainly due to the failure of many primary-level pediatricians to distinguish asthma from common respiratory diseases, such as bronchitis and pneumonia. Such misdiagnoses often lead to the abuse of antibiotics and systemic glucocorticoids. Additionally, if asthma is not diagnosed early, chronic airway inflammation results in lesions that not only hamper children's athletic abilities, but serve as the primary cause for adult chronic airway diseases, such as chronic obstructive pulmonary disease (COPD).
Methods:
A number of machine learning-based models including CatBoost, Logistic Regression, Naïve Bayes, and Support Vector Machines (SVM) have been developed to identify asthma via utilizing retrospective electronic medical records (EMRs) of patients. These models were evaluated independently using EMRs from both the Pulmonology Department and other departments of the Children's Hospital, Zhejiang University School of Medicine, China.
Results:
Two independent test sets were applied for performance evaluation. TestSet-1 consisted of 325 positive asthma cases and 428 negative cases from the Pulmonology Department. TestSet-2 was composed of 2,123 cases from non-pulmonology departments, and included 337 positive and 1,786 negative cases. Experimental results showed that the CatBoost model outperformed other models on both test sets with an accuracy of 84.7% and an area under the curve (AUC) of 90.9% on TestSet-1, and an accuracy of 96.7% and an AUC of 98.1% on TestSet-2.
Conclusions:
The artificial intelligence (AI) model could rapidly and accurately identify asthma in general medical wards of children, and may aid primary pediatricians in the correct diagnoses of asthma. It possesses great clinical value and practical significance in improving the control rate of asthma in children, optimizing medical resources, and limiting the abuse of antibiotics and systemic glucocorticoids.
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