## 5 Discussion
The use of different criteria for categorizing MoCA scores makes it challenging to compare results across studies. Furthermore, the normative data for the MoCA vary with age [16], making it difficult to establish a universal cutoff value for dichotomizing patients. In contrast, our work leverages continuous MoCA scores to predict cognitive decline in PD, avoiding the pitfalls of discretization and allowing for a more nuanced understanding of cognitive changes. We identified only a single study that predicted cognitive outcomes using MoCA changes as a continuous variable. In their work, [PERSON] et al. (2019) [11] predicted the MoCA score at year 4 using non-motor data obtained at baseline and at the first year from the PPMI database (n = 184 PD). Their best approach used NSGAII [17] for feature subset selection (selecting six features), combined with LOLIMOT [18] for prediction, achieving a MAE of 1.68 \(\pm\) 0.12. When tested on independent data (n = 308 PD), they achieved a MAE of 1.65. Using the same cohort of patients, we improved their predictions by obtaining lower MAEs for the year 4 MoCA change (1.55 \(\pm\) 0.23) using demographic and clinical variables. In contrast to their results, the LASSO-LARS algorithm provided the best results in our work. LASSO-LARS has advantages over other algorithms, as it automatically adjusts the number of variables using a regularization method. This feature is particularly useful when dealing with high-dimensional databases, as it identifies a subset of predictors that capture the most relevant information while discarding redundant information. In contrast, models such as DT and RF showed worse performances, likely due to their sensitivity to the high-dimensional and potentially noisy data. SVM models, while effective in some scenarios, required extensive hyperparameter tuning and were more computationally intensive, making them less practical for clinical implementation. Finally, LR struggled with the high dimensionality of the data and did not incorporate the regularization needed to handle irrelevant or less important variables, leading to less accurate predictions. This study enhances our understanding of cognitive decline in PD by demonstrating that mid-term cognitive deterioration can be effectively predicted using early short-term changes in MoCA scores. Our results indicate that incorporating baseline variables from specific cognitive domains significantly improves predictive performance. By averagingMoCA scores over multiple time points, our approach offers robust and scalable predictions, reducing inconsistencies and increasing reliability. These findings suggest that early cognitive assessments are crucial for predicting cognitive decline, allowing for timely interventions and better patient management in clinical settings. To transform these findings into practical clinical applications, several steps must be taken. First, integrating ML models into clinical workflows requires developing user-friendly software tools that can be easily adopted by healthcare providers. These tools should provide clear guidelines on how to input patient data and interpret model predictions. Training and education programs for clinicians will be essential to ensure they understand the utility and limitations of these predictive models. Potential challenges include ensuring the quality and consistency of the input data, as variability in data collection can affect model accuracy. Standardizing data collection protocols across different clinical settings will be necessary to mitigate this issue. One potential limitation of this study is that the results are based on the PPMI cohort of early-stage PD patients, so caution should be taken when extrapolating the findings to other populations or later stages of PD. Additionally, the exclusion criteria applied during the data cleaning process might impact the generalizability of the study results.