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Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental
Zineb Jeddi1, Ihsane Gryech1,2, Mounir Ghogho1,3
1TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco.
Insights
Machine learning models predict childhood asthma using risk factors. Random forest achieved 84.9% accuracy, identifying key prenatal and environmental factors for prevention.
Area of Science:
- Pediatric Allergy and Immunology
- Computational Epidemiology
- Environmental Health
Background:
- Childhood asthma prevalence and risk factors differ globally, with limited data in Morocco.
- Research on childhood asthma in Morocco is hindered by data scarcity.
- Understanding regional risk factors is crucial for effective public health strategies.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting childhood asthma in Morocco.
- To identify significant risk factors associated with childhood asthma.
- To explore the utility of computational methods in addressing data limitations in pediatric research.
Main Methods:
- A prospective study involving 202 children (with and without asthma).
- Chi-squared tests for initial factor association assessment.
- Machine learning models including logistic regression, decision trees, random forest, and support vector machines for prediction.
- Chi-squared feature selection identified 19 significant factors from 36 variables.
Main Results:
- 19 factors significantly associated with childhood asthma (p < 0.05), including family history of atopy, environmental exposures (mites, cold air, odors, mold), birth mode, breastfeeding, and early life habits.
- Random forest model demonstrated the highest predictive accuracy (84.9%).
- Logistic regression (82.57%), support vector machine (82.5%), and decision trees (75.19%) also showed strong predictive performance.
Conclusions:
- Machine learning models effectively predict childhood asthma using identified risk factors.
- Key modifiable maternal and prenatal risk factors for childhood asthma were identified.
- Increased awareness of these avoidable risk factors is essential for asthma prevention strategies.
Abstract:
The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (p-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child's environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors.
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