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Updated: May 31, 2026
04:04
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Key Symptoms in Bronchial Asthma Diagnosis: Identification via Decision Tree Modeling
Tian-Yue Wang1, Guo-Liang Liu2, Yu-Ping Jia2
1China Japan Friendship Institute of Clinical Medicine Research, Beijing, People's Republic of China.
Summary
A decision tree model effectively identified key clinical features for diagnosing bronchial asthma. This approach achieved high accuracy, aiding physicians in diagnosis and optimizing clinical workflows.
Area of Science:
- Respiratory Medicine
- Clinical Informatics
- Machine Learning in Healthcare
Background:
- Accurate diagnosis of bronchial asthma relies heavily on clinical features.
- The relative diagnostic importance of these features has not been clearly established.
Purpose of the Study:
- To evaluate the importance of clinical features in diagnosing bronchial asthma.
- To develop a decision tree model for asthma diagnosis.
Main Methods:
- Retrospective analysis of 4,417 patients with asthma-like symptoms (2005-2020).
- Development of a symptom-based decision tree model using the Exhaustive CHAID algorithm.
- Evaluation of diagnostic performance using ROC curves, sensitivity, specificity, PPV, and NPV.
Main Results:
- Identification of 12 key clinical variables using the decision tree model.
- High diagnostic accuracy of 93.9% (95% CI: 93.17-94.58%).
- Area under the ROC curve of 0.973 (95% CI: 0.968-0.978), indicating excellent performance.
Conclusions:
- Decision tree models effectively utilize key clinical symptoms for asthma diagnosis.
- These models can enhance physician clinical reasoning and optimize diagnostic workflows.
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