Related Experiment Video
Updated: Jan 9, 2026
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Performance analysis of artificial intelligence-based classification models for diagnosing asthma in children
Gokhan Yorusun1, Ozge Yilmaz Topal1, Cagatay Berke Erdas2
1Ankara Bilkent City Hospital Pediatric Immunology and Allergy, Ankara, Turkiye.
Insights
Artificial intelligence, specifically machine learning models, shows significant promise in accurately diagnosing pediatric asthma. Advanced algorithms like Gradient Boosting achieved high diagnostic performance, improving efficiency in identifying childhood asthma.
Area of Science:
- Pediatric Pulmonology
- Medical Informatics
- Computational Medicine
Background:
- Asthma is a prevalent respiratory condition in children, characterized by symptoms like cough and wheezing.
- Accurate diagnosis of pediatric asthma is crucial for timely and effective management.
- This study investigates the utility of artificial intelligence in enhancing diagnostic precision for childhood asthma.
Purpose of the Study:
- To evaluate the effectiveness of various machine learning models in diagnosing pediatric asthma.
- To compare the performance of different algorithms in distinguishing asthma from non-asthmatic chronic cough in children.
- To identify key clinical predictors for pediatric asthma using data-driven approaches.
Main Methods:
- A cohort of 900 children (aged 6-18 years) with chronic cough was analyzed.
- Eight machine learning models were applied to demographic, clinical, and pulmonary function data.
- Model performance was assessed using metrics including F1 score and ROC AUC.
Main Results:
- Gradient Boosting, Random Forest, and AdaBoost models exhibited high diagnostic performance (F1 scores > 0.969, ROC AUC > 0.995).
- Exercise-induced cough and recurrent bronchiolitis were identified as significant asthma predictors.
- k-Nearest Neighbors showed the lowest accuracy, highlighting variability in model effectiveness.
Conclusions:
- Machine learning algorithms demonstrate substantial potential for improving the accuracy and efficiency of pediatric asthma diagnosis.
- The findings suggest that AI-powered tools can aid clinicians in diagnosing childhood asthma more effectively.
- Further research is warranted to validate and implement these AI models in clinical practice.
Introduction:
Asthma is a common childhood disease with symptoms such as cough, wheezing, and shortness of breath. This study evaluated the role of artificial intelligence in improving diagnostic accuracy in children.
Methods:
We included patients aged 6-18 years evaluated at our clinic between January 2024 and January 2025. Those with chronic cough were classified as asthma or non-asthma based on final diagnosis. Demographic, clinical, and pulmonary function data were collected. Eight machine learning models Gradient Boosting, AdaBoost, Random Forest, Logistic Regression, Linear Discriminant Analysis, Decision Tree, k-Nearest Neighbors, and Naive Bayes were applied, and their performance was assessed using accuracy, precision, recall, F1 score, ROC AUC, and MCC.
Results:
A total of 900 children were included, with 450 diagnosed with asthma and 450 with non-asthmatic chronic cough. Males comprised 52.9% of the cohort. Feature importance analysis highlighted exercise-induced cough and recurrent bronchiolitis as the most significant predictors for asthma. Gradient Boosting demonstrated the highest diagnostic performance (F1: 0.974, ROC AUC: 0.997), followed closely by Random Forest (F1: 0.972, ROC AUC: 0.997) and AdaBoost (F1: 0.969, ROC AUC: 0.995). Logistic Regression, LDA, Decision Tree, and Naive Bayes showed moderate performance, while KNN had the lowest accuracy (F1: 0.566, ROC AUC: 0.615), indicating variable effectiveness among models.
Discussion:
Machine learning algorithms show promise in improving diagnostic accuracy and efficiency in pediatric asthma, though further research is needed.
Related Concept Videos
01:26Asthma-II: Pathophysiology and Classification
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
01:30Asthma-IV: Diagnostic and Management
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:
01:24Asthma-III: Symptoms and Complications
Classification of Asthma
01:29Asthma-I: Introduction
01:30Asthma-IV: Nursing Management
First, in...
01:27Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies
Medical History