An ever-growing catalog of human variants is hosted in the ClinVar database. In this database, submissions on a variant are combined into a multi-submitter record and in the case of discordance in variant classification between submitters, the record is labelled as conflicting. We used ClinVar data to identify characteristics that would make variants more likely to be associated with the conflict class of variants. Further, we used the Extreme Gradient Boosting (XGBoost) algorithm to train classifier models to provide prediction of classification discordance for single submission variants in ClinVar database.We showed that population allele frequency, the gene harboring the variant, variant type, consequence on protein, variant deleteriousness score, first submitter identity and submission count are associated with conflict in variant classification. Using such features, the optimized classifier showed accuracy on the test set of 88% with the weighted average of precision, recall and f1-score of 0.84, 0.88 and 0.85, respectively.There are pronounced associations between variant classification discordance and allele frequency, gene type, and the identity of the first submitter. We provide the predicted discordance status for single-submitter variants deposited in ClinVar. Our approach can be used to assess whether single-submitter variants are likely to be supported, or in conflict with, future entries; this knowledge might help laboratories with clinical variant assessment.