## 6 Conclusions  
This study employed ML techniques to predict mid-term cognitive outcomes in early PD patients using demographic and clinical variables from the PPMI database. Cognitive impairment is a prevalent and highly disabling feature of PD, significantly impacting patients' quality of life. Certain PD patients are predisposed to experience a more rapid cognitive decline and earlier onset of dementia. However, accurately predicting the rate of cognitive deterioration from the early stages of PD remains a clinical challenge that has not been fully addressed. The performance of various algorithms was compared, with the LASSO algorithm consistently outperforming others, achieving the lowest MAE for predictions of year 4 and 5 MoCA scores. The importance of the predictors was assessed using SHAP values and LASSO coefficients; revealing that the baseline MoCA score and MoCA deterioration at one-year intervals had the highest coefficients, indicating their significant contributions to mid-term MoCA score predictions. A LASSO model trained to predict the mean MoCA deterioration at year 4 and year 5 demonstrated a 19% improvement in MAE, highlighting its robustness. In conclusion, our study demonstrates that using a limited number of objective and easily obtainable predictor variables can lead to the accurate prediction of cognitive outcomes in early-stage PD patients, providing a promising avenue for the development of ML models for clinical use. However, this study has some limitations: it is based on the PPMI cohort of early-stage PD patients, so caution is needed when generalizing to other populations or later stages of PD. This analysis relied on clinical data as its predictors, but incorporating biological specimens like blood biomarkers could enhance patient stratification. Additionally, a sex-based analysis was not conducted as sex was not a significant variable. Conceptualization, all authors; methodology, M.M.-E., O.A., IG. ([PERSON]) and J.M. ; software, M.M.-E.; validation, IG. ([PERSON]) and A.M.-G.; formal analysis, all authors; investigation, M.M.-E.; resources, I.G. ([PERSON]) and I.G. ([PERSON]); data curation, M.M.-E.; writing--original draft preparation, M.M.-E. and A.M.-G.; writing--review and editing, all authors; visualization, M.M.-E.; supervision, J.C.G.-E.; project administration, M.M.-E.; funding acquisition, O.A., J.M. and I.G. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** [PERSON] is the recipient of a predoctoral fellowship from the Basque Government (Grant PRE-2022-1-0204). This work was funded by Grant PID2021-123087 OB-I00, MICIU/AEI/10.13039/501100011033, and FEDER, UE (Grant Recipient: [PERSON] and [PERSON]), as well as the Department of Economic Development and Competitiveness (ADIAN, IT1437-22) of the Basque Government (Grant Recipient: [PERSON]). **Institutional Review Board Statement:** (1) Approval Code: The PPMI study has been registered on ClinicalTrials.gov with the identifier NCT01141023, which serves as the approval reference for this specific study. (2) Name of the Ethics Committee or Institutional Review Board: The study was approved by the local ethics committees of the participating sites. A list of these sites and their respective ethics committees can be found at [[https://www.ppmi-info.org/about-ppmi/pmmi-clinical-sites](https://www.ppmi-info.org/about-ppmi/pmmi-clinical-sites)]([https://www.ppmi-info.org/about-ppmi/pmmi-clinical-sites](https://www.ppmi-info.org/about-ppmi/pmmi-clinical-sites)). Participants provided written, informed consent to participate. (3) Date of Approval: The exact date of approval for each site's ethics committee is not provided in the public domain. (4) Adherence to the Declaration of Helsinki: The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice (GCP) guidelines after approval of the local ethics committees of the participating sites.