Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors

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.

Involvement of the Intestinal Microbiota in the Appearance of Multiple Sclerosis: Aloe vera and Citrus bergamia as Potential Candidates for Intestinal Health

“…Conversely, Dalla Costa et al have demonstrated that the type of delivery and the type of lactation affect the age of onset of MS. In particular, caesarean section and formula milk anticipate the age of onset of the disease [196]. Interpretation of these results could be influenced by the fact that both caesarean section and formula milk lead to an imbalance in the development of the microbiota, which would participate in the alteration of the immune system, culminating in the onset of an autoimmune pathology such as MS.…”

Artificial Intelligence and Pediatrics: Synthetic Knowledge Synthesis

“…Decision trees were successfully used to associate demographic features with allergic outcomes during the allergic march to assess the possibility of allergy transfer to asthma in children with respect to race [36]. Decision trees were also used to explore the relationship between various risk factors and childhood asthma [37]. Neural networks were used for a cough sound analysis to differentiate pneumonia from asthma [38].…”

Predicting the Risk of Asthma Development in Youth Using Machine Learning Models

“…The recent development of artificial intelligence techniques (especially machine learning and deep learning), as well as the data availability of detailed patient information, empowers the prediction and risk assessment of various chronic diseases. For example, machine learning has been employed in predicting cardiovascular diseases (7)(8)(9). While many machine learning models have been successfully built for predicting asthma exacerbations among asthmatic patients (10)(11)(12)(13)(14)(15)(16), fewer studies have been conducted on predicting asthma risk using various datasets especially publicly available data, such as the 2019 Michigan BRFSS data and a small dataset of 202 children from Ibn Sina Hospital Center in Morocco (7,8).…”