## 4 Results  
The five algorithms were trained to predict the cognitive deterioration (based on the changing score of the MoCA test) of each patient in their fourth and fifth year post diagnosis, using demographic and clinical variables. These algorithms were trained using baseline variables (BL), the deterioration of variables from the baseline to first year (\(\Delta\)Y1), or a combination of both (BL + \(\Delta\)Y1). The results are presented in Figure 2. In total, 30 variables were used for BL and \(\Delta\)Y1, while 49 variables were used for BL + \(\Delta\)Y1. In all three cases, the 10 demographic variables and the olfactory variable were common. Overall, the BL + \(\Delta\)Y1 combination with the LASSOLARS algorithm consistently outperformed the other models, producing the lowest MAEs of \(1.55\ \pm\ 0.23\) and \(1.56\ \pm\ 0.19\) for the fourth- and fifth-year predictions, respectively. Despite having twice as much clinical information, the improvement obtained using BL + \(\Delta\)Y1 was not significantly large. Additionally, some inconsistencies were observed in the results. While the \(\Delta\)Y1 data appeared to be more informative than the BL data, with a lower error for the fourth-year prediction of cognitive deterioration (Figure 2a), the opposite was observed for the fifth-year MoCA change prediction (Figure 2b). We further analyzed the LASSOLARS algorithm with BL + \(\Delta\)Y1, as it provided the best results. Figure 3 shows the relationship between the predicted and the actual values. Figure 4 presents a feature importance analysis: part (a) displays the importance of the domains using SHAP values, excluding the MoCA variable from the cognitive domain. The clinical variables at the baseline and their first-year change are grouped accordingly; part (b) extends this view to the importance of the variables through the LASSOLARS coefficients, focusing on those with a mean coefficient greater than 0.1 in any year. The race variable is omitted due to its highly imbalanced distribution and minimal impact on the study's cases. From Figure 4, it is evident that the early MoCA score is the most informative, followed by the cognitive group. The high positive coefficient of \(\Delta\)MoCA suggests that MoCA deterioration in the first year is a clear indicator of further deterioration in subsequent years. Conversely, the negative coefficient of MoCA indicated that a greater deterioration is probable if the patient shows a high MoCA score at baseline. Interestingly, the olfactory group (UPSIT) appears to be the least informative across both years. However, the importance of the remaining tests is not consistent. For example, when predicting the deterioration at year 4, the first-year change in the BJLOT test and the baseline values of the SCOPA-AUT and HY tests are relevant, but they are not when predicting the deterioration at year 5. Figure 2: Mean absolute error (MAE) generated when predicting cognitive deterioration (**a**) at year 4 and (**b**) at year 5. The bars represent the mean of the 10 folds, while the lines indicate the standard deviation. The MAE values are estimated using the 5 algorithms and the clinical variables at baseline (BL), the deterioration of the clinical variables from baseline to the first year (\(\Delta\)Y1), and a combination of both (BL + \(\Delta\)Y1). The number of predictors used in each analysis is shown in parentheses. Abbreviations: LR = linear regression, DT = decision tree, SVM = Support Vector Machine, LAS = Least Absolute Shrinkage and Selection Operator with Least Angle Regression, RF = Random Forest. Given the inconsistencies in the variables used for predicting MoCA changes in years 4 and 5, likely due to the intrinsic variability in the test, we trained the LASSOLARS model to predict the mean MoCA deterioration over years 4 and 5 as a surrogate for mid-term cognitive decline. As a result, we achieved a 19% improvement in its MAE (see Table 2). Figure 4: Feature importance analysis of the LASSOLARS model for predicting cognitive deterioration at year 4 and year 5.