The incidence of childhood atopic dermatitis (AD) and allergic rhinitis (AR) is increasing. This warrants development of measures to predict and prevent these conditions. We aimed to investigate the predictive ability of a spectrum of data mining methods to predict childhood AD and AR using longitudinal birth cohort data. We conducted a 14-year follow-up of infants born to pregnant women who had undergone maternal examinations at nine selected maternity hospitals across Taiwan during 2000–2005. The subjects were interviewed using structured questionnaires to record data on basic demographics, socioeconomic status, lifestyle, medical history, and 24-h dietary recall. Hourly concentrations of air pollutants within 1 year before childbirth were obtained from 76 national air quality monitoring stations in Taiwan. We utilized weighted K-nearest neighbour method (k = 3) to infer the personalized air pollution exposure. Machine learning methods were performed on the heterogeneous attributes set to predict allergic diseases in children. A total of 1439 mother-infant pairs were recruited in machine learning analysis. The prevalence of AD and AR in children up to 14 years of age were 6.8% and 15.9%, respectively. Overall, tree-based models achieved higher sensitivity and specificity than other methods, with areas under receiver operating characteristic curve of 83% for AD and 84% for AR, respectively. Our findings confirmed that prenatal air quality is an important factor affecting the predictive ability. Moreover, different air quality indices were better predicted, in combination than separately. Combining heterogeneous attributes including environmental exposures, demographic information, and allergens is the key to a better prediction of children allergies in the general population. Prenatal exposure to nitrogen dioxide (NO2) and its concatenation changes with time were significant predictors for AD and AR till adolescent.
日期:
2021-07-10
關聯:
Science of the Total Environment. 2021 Jul 10;777:Article number 145982.
