## 2 Related Works  
Several studies have addressed the problem of ML for the prediction of cognitive deterioration in patients with early PD. One of the main databases in the field is PPMI, which contains a cohort of recently diagnosed PD participants. This PD cohort is comprised of several subgroups, which include idiopathic PD participants and several genetic PD participants. Idiopathic means that a disease has no identifiable cause; therefore, its evolution is less predictable because there are no defined phenotypes. However, none of the studies specify having selected this subgroup. In the study by [PERSON] et al. (2022) [9], a multivariate ML model was developed to predict cognitive impairment and dementia conversion using clinical, biofluid, and genetic/epigenetic measures taken from baseline. The researchers found that clinical variables alone performed best, with only marginal improvements seen when additional data types were included. This indicates that while the inclusion of multi-modal data can enhance prediction, clinical measures remain crucial for accurate forecasts. Similarly, [PERSON] and [PERSON] (2024) [10] compared the effectiveness of different cognitive scales, specifically the Montreal Cognitive Assessment (MoCA) and the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-I), combined with DAT SPECT imaging and clinical biomarkers in predicting cognitive decline over five years. Their findings revealed that the MoCA was the more effective predictor. Additionally, the incorporation of deep radiomic features from DAT SPECT significantly improved its predictive performance, highlighting the importance of advanced imaging techniques in enhancing the accuracy of predictions. The research conducted by [PERSON] et al. (2019) [11] focused on optimizing feature selection and machine learning algorithms to predict MoCA scores in PD patients. By using robust predictor algorithms and feature subset selection, they achieved high predictive accuracy with a minimal set of features. This study underscores the importance of feature selection, showing that effective prediction can be accomplished with fewer, well-chosen variables. [PERSON] et al. (2023) [12] combined handcrafted radiomics, deep features from DAT SPECT, and clinical features to predict cognitive outcomes in PD patients. They found that clinical features were vital for prediction, and the combination of these with imaging features yielded the highest accuracy. This demonstrates the critical role of integrating multi-modal data, but also points to the necessity of identifying which features contribute most to predictive performance. [PERSON] et al. (2023) [13] developed a multimodal ML model to predict continuous cognitive decline using clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes from T1-weighted MRI, and genetic variants. The best-performing model combined clinical and CSF biomarkers, suggesting that [PERSON]'s disease-related pathology significantly contributes to cognitive decline in PD. This study highlights the importance of considering biomarkers associated with other neurodegenerative diseases in predicting cognitive outcomes in PD. [PERSON] et al. (2023) [14] approached the prediction of long-term cognitive decline through a Deep Neural Network with transfer learning from Alzheimer's disease to PD. This model used brain MRI and clinical data, demonstrating that knowledge transfer between neurodegenerative diseases is feasible and can improve prediction in PD. Their results showed that leveraging models pre-trained on related diseases could yield significant predictive power, even when direct data on the target disease are limited. Despite these advancements, challenges remain. Current studies primarily focus on categorizing PD patients into distinct groups based on their cognitive outcomes. This discrete approach overlooks the continuous nature of cognitive decline, which varies significantly among individuals.