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Childhood stunting is a serious global public health issue that exhibits local spatial variations. Previous studies have used traditional statistical methods to identify stunting risk factors, and little is known about the application and usefulness of spatial machine learning techniques in identifying localized stunting risk factors based on complex datasets. This study assesses the performance o
