## 5 Discussion  
In this study, we used ML techniques to predict mid-term cognitive outcomes in early PD patients, using demographic and clinical variables from the early stages of PD as predictors. We tested several algorithms and selected LASSO, which performed the best based on the MAE metric. Predicting cognitive decline is of the utmost importance in PD research. While the diagnosis of PD is primarily based on motor symptoms, cognitive impairment is increasingly recognized as a common and often debilitating non-motor symptom of the disease [2]. In fact, up to 70% of PD patients will develop cognitive impairment over the course of the disease [3]. Given that the severity and pattern of cognitive impairment in PD can vary widely between individuals [4], identifying patients at greater risk of cognitive decline is crucial for optimizing patient care and patient stratification for clinical trials. According to our results, the baseline global cognition (baseline MoCA score) and cognitive decline (MoCA deterioration) after one year were the most determinant factors for predicting mid-term cognitive scores. This aligns with a previous study that found baseline MoCA scores to be highly associated with cognitive profiles at 8 years on in the PPMI cohort [9]. To better understand how different factors collectively influence cognitive  
Figure 6: Relation between the LASSO coefficients used for predicting MoCA deterioration at different time points. The graph is divided into two sections, with the lower section providing a zoomed-in view. Variables with a \(\Delta\) symbol represent changes in the first year, while those without it represent baseline values. At the bottom of the graph, the mean absolute error (mean \(\pm\) standard deviation) is displayed. Abbreviations: HVLT1 = Hopkins Verbal Learning Test-Revised immediate/total recall, HVLT2 = Hopkins Verbal Learning Test-Revised discrimination recognition, TD-PIGD3 = indeterminate at year 1 in TD-PIGD classification; see the rest in Table 1.  
deterioration, we conducted a deeper analysis of the relative importance of these variables compared to other clinical variables. Using SHAP values and LASSO-LARS coefficients, we assessed the importance of various predictors. In addition to the baseline MoCA score and one-year MoCA deterioration, other cognitive variables were also relevant for the final prediction. However, the models for year 4 and year 5 did not completely agree on their importance. This inconsistency may be attributed to two reasons: the similar importance of different domains and the potential overfitting of the models to noisy variations in the prediction targets. Indeed, obtaining reliable measurements of cognition for constructing models is challenging due to the inherent noise present in clinical tests. Averaging cognitive scores from multiple time points can help mitigate this issue. In our study, we found that averaging the MoCA scores from years 4 and 5 resulted in lower MAE values than using individual time points. This approach resolves the inconsistencies found in the models fitted to year 4 and 5, reinforcing the importance of the predictors selected by both models--the baseline values of SDMT, HVLT immediate/total recall, LNS, BJLOT, and SFT--and including variables with lower importance levels that were selected by only one of the models--the baseline values of HVLT discrimination recognition, SCOPA, and REM and 1-year deterioration of BJLOT and MDS-UPDRS total. Several previous studies have attempted to predict cognitive outcomes in PD using ML algorithms [6; 7; 11]. However, the prevailing trend in the field is to categorize PD patients into distinct groups based on their cognitive outcomes, overlooking the continuous nature of cognitive changes among patients. For instance, [PERSON] et al. (2019) [6] categorized MoCA score changes into severe and mild-to-moderate cognitive impairment, while [PERSON] et al. (2020) [7] established a cutoff score of 26/27 for MoCA, creating two categories: normal cognition and mild cognitive impairment (MCI).