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Large-scale data sources, remote sensing technologies, and superior computing power have tremendously benefitted to environmental health study. Recently, various machine-learning algorithms were introduced to provide mechanistic insights about the heterogeneity of clustered data pertaining to the symptoms of each asthma patient and potential environmental risk factors. However, there is limited information on the performance of these machine learning tools. In this study, we compared the performance of ten machine-learning techniques.