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Fig. 1 | Journal of Eating Disorders

Fig. 1

From: Using machine learning to explore core risk factors associated with the risk of eating disorders among non-clinical young women in China: A decision-tree classification analysis

Fig. 1

Decision tree for classifying at-risk of EDs. Note: Figure shows the classification tree for at-risk of EDs based on the training subsample of the overall dataset (n = 581 of 830). Total_BIAAQ = total score of Body Image Acceptance and Action Questionnaire. Total_K = total score of Kessler Scale to assess psychological distress. Total_EDI_BD = Body Dissatisfaction subscale of the Eating Disorder Inventory. For each internal node, the first line refers to a decision rule with a selected attribute. For example, the root node indicates a decision rule that the attribute body image inflexibility is smaller than or equal to 15.01. For a node with branches, its left child node follows the decision rule in the parent node, whereas its right child follows the complement of the decision rule. The second line of each internal node indicates the percentage of samples involved in this node. The third line refers to the percentages of positive samples (i.e., samples at-risk of EDs) and that of negative samples (i.e., samples without at-risk of EDs) within each node. The shade of color refers to the purity of each node, implying the extent of a mixture of groups for a subset of samples. The dark color means most samples belong to one group. Lastly, class in each box indicates whether high risk of EDs is more prevalent in a node. Blue boxes with class = yes indicate at-risk of EDs is more prevalent, whereas orange boxes with class = no indicate the subgroups contain more people with low risk of EDs, based on EDE-QS scores

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