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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children
Piyush Bhardwaj1, Ashish Tyagi2, Shashank Tyagi3
1Centre for Advanced Computational Solutions (C-fACS), Department of Molecular Biosciences, Lincoln University, Lincoln, Christchurch, New Zealand.
Insights
Machine learning effectively classifies allergic and non-allergic asthma in preschool children using key features. This approach aids in diagnosing this common childhood airway illness.
Area of Science:
- Pediatric Pulmonology
- Computational Medicine
- Allergy and Immunology
Background:
- Asthma is a prevalent chronic airway disease in young children, presenting diagnostic challenges due to its varied nature.
- Differentiating between allergic and non-allergic asthma is crucial for effective treatment strategies.
Purpose of the Study:
- To evaluate various machine learning models for classifying predominantly allergic and non-allergic asthma in preschool children.
- To identify the most effective machine learning model utilizing a minimal set of predictive features.
Main Methods:
- Utilized the Frankfurt dataset comprising 205 asthma patients, with 127 selected post-pre-processing.
- Employed Random Forest and Chi-square for feature selection from 63 initial features.
- Trained and tested six machine learning models (Random Forest, XGBoost, SVM, AdaBoost, Extra Tree, Logistic Regression) using 10-fold cross-validation.
Main Results:
- Identified nine key features including age, C-reactive protein, and specific pre-medications.
- The Support Vector Machine (SVM) with a linear kernel achieved the highest accuracy (77.8%) in differentiating asthma types.
- Logistic regression demonstrated strong performance as the second-best classifier with 76.2% accuracy.
Conclusions:
- Machine learning models can accurately classify predominantly allergic and non-allergic asthma in preschool children.
- A reduced set of nine features is sufficient for effective classification, simplifying diagnostic approaches.
Objective:
Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective one to classify two forms of asthma in preschool children (predominantly allergic asthma and non-allergic asthma) using a minimum number of features.
Methods:
After pre-processing, 127 patients (70 with non-allergic asthma and 57 with predominantly allergic asthma) were chosen for final analysis from the Frankfurt dataset, which had asthma-related information on 205 patients. The Random Forest algorithm and Chi-square were used to select the key features from a total of 63 features. Six machine learning models: random forest, extreme gradient boosting, support vector machines, adaptive boosting, extra tree classifier, and logistic regression were then trained and tested using 10-fold stratified cross-validation.
Results:
Among all features, age, weight, C-reactive protein, eosinophilic granulocytes, oxygen saturation, pre-medication inhaled corticosteroid + long-acting beta2-agonist (PM-ICS + LABA), PM-other (other pre-medication), H-Pulmicort/celestamine (Pulmicort/celestamine during hospitalization), and H-azithromycin (azithromycin during hospitalization) were found to be highly important. The support vector machine approach with a linear kernel was able to diffrentiate between predominantly allergic asthma and non-allergic asthma with higher accuracy (77.8%), precision (0.81), with a true positive rate of 0.73 and a true negative rate of 0.81, a F1 score of 0.81, and a ROC-AUC score of 0.79. Logistic regression was found to be the second-best classifier with an overall accuracy of 76.2%.
Conclusion:
Predominantly allergic and non-allergic asthma can be classified using machine learning approaches based on nine features.
Abstract:
Supplemental data for this article is available online at at www.tandfonline.com/ijas .
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