## 1 Introduction  
Artificial Intelligence (AI) and machine learning (ML) have revolutionized various sectors, including healthcare, by enabling the analysis of large datasets. Recent studies [1] highlight the transformative impact of big data and AI on healthcare, emphasizing the potential of AI to improve the diagnosis, treatment, and management of diseases through the intelligent analysis of complex datasets.  
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by a loss of dopaminergic neurons in the substantia nigra. The cardinal motor symptoms for a PD diagnosis include tremor, rigidity, and bradykinesia [2]. However, cognitive impairment is a common and disabling feature of PD, affecting up to 70% of patients within 10 years of their diagnosis [3]. Some PD patients are more susceptible to an early onset of dementia, but the current lack of clinically validated biomarkers hinders an accurate prognostication of the rate of cognitive decline [4]. Given the substantial impact of PD-associated dementia on independence, caregiver burden, and mortality, there is growing interest in identifying individuals with early-stage PD who are at higher risk of rapid cognitive decline to facilitate intervention studies.  
The Montreal Cognitive Assessment (MoCA) test is a widely used screening tool for determining cognitive impairment in PD. It is a brief cognitive assessment that measures multiple cognitive domains, including attention, memory, language, and visuospatial skills. The MoCA has been shown to have high sensitivity and specificity for detecting cognitive impairment in PD [5].  
Focusing on PD, ML techniques have advanced significantly in recent years and show promise for predicting cognitive decline over time. Previous studies using ML methods have developed accurate prediction models with high degrees of accuracy, sensitivity, and specificity for mild cognitive impairment and dementia in de novo PD patients, suggesting that ML techniques may have a significant potential to predict cognitive outcomes [6; 7]. However, these models relied on the discretization of cognitive outcomes, single-time-point data, and complex multi-modal data, which pose challenges for their scalability in clinical settings (see Section 2 for more information).  
In this study, our objective was to predict mid-term cognitive outcomes in early PD patients from the Parkinson's Progression Markers Initiative (PPMI) [8] cohort using multiple ML methods. To achieve this, we evaluated the effectiveness of several easily measurable demographic and clinical variables at anticipating changes in MoCA scores over the 4 to 5 years following the baseline visit. We relied on baseline and 1-year changes in demographic, cognitive, olfactory, autonomic, neuropsychiatric, sleep disorder, and motor symptoms as predictor variables. Additionally, we assessed the contribution of each category of variable to the prediction, enhancing the model's transparency and explicability for clinical stakeholders. The implications of this study are significant, as they provide a foundation for identifying patients with early PD who are at higher risk for cognitive decline, enabling timely interventions and better patient management.  
The remainder of the paper is structured as follows: in Section 2, we explain open research challenges and how they are addressed in this study. In Section 3, we describe the database, data pre-processing, ML methods, and evaluation metrics used in the experiments. In Section 4, we present the results of the experiments. In Section 5, we compare our findings with those of other studies and discuss the limitations of our work. Finally, in Section 6, we offer some concluding remarks.