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. Author manuscript; available in PMC: 2019 Aug 1.
Published in final edited form as: Autism Res. 2018 May 7;11(8):1120–1128. doi: 10.1002/aur.1960

Figure 2. Random forest classifier performance based on lifetime ICD-9 codes, V-codes, and E-codes.

Figure 2

This receiver operating characteristic (ROC) curve provides a comprehensive visualization of the performance of our predictive model. The area under the ROC curve (AUC) illustrates how well our random forest algorithm can distinguish between decedents with ASD and matched decedent community controls. The ROC curve displays the false-positive rate, or 1 – specificity versus sensitivity. The current classifier has an AUC of 0.88 which is significantly higher than the baseline AUC of 0.5.