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Background: Mild cognitive impairment in Parkinson's disease (PD-MCI) includes deficits in different cognitive domains, and one domain to explore for neurocognitive impairment following the DSM-V is social cognition. However, this domain is not included in current criteria for PD-MCI diagnosis. Moreover, tests vary across studies. It is, therefore, crucial to optimize cognitive assessment in PD-MCI. We aimed to do so by using Machine Learning., Methods: 275 PD patients were included. Four cognitive batteries were created: two Standard ones (Levels I and II), applying current criteria and "traditional" tests; two Alternative ones (Levels I and II), which incorporated a test of social cognition. These batteries were included in the Random Forest (RF) classifier. To assess RF performance, the AUC was considered, and the Variable Importance Index was estimated to understand the contribution of each test in PD-MCI classification., Results: Standard Level I and II showed an AUC of 0.852 and 0.892, while Alternative Level I and II showed an AUC of 0.898 and of 0.906. Variable Importance Index revealed that TMT B-A, Ekman test, RAVLT-IR, MoCA, and Action Naming were tests that most contributed to PD-MCI classification., Conclusion: The Alternative level I assessment demonstrated a similar classification capacity to the Standard level II assessment. This finding suggests that in the cognitive assessment of PD patients, it is crucial to consider the most affected cognitive domains in this clinical population, including social cognition. Taken together, these results suggest to revise current criteria for the diagnosis of PD-MCI., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)