## 4 Results
(**a**) Importance of domains: SHAP values are presented, with the domains ordered from the most to least important in predicting deterioration. (**b**) Importance of variables: This graph displays the coefficients of the most significant variables in the LASSOLARS model. The bars represent the mean of the 10 folds, while the black lines indicate the standard deviation. \(\Delta\) indicates that this is the first-year change, while all the others are baseline values. Variables are sorted based on their importance in year 4, considering both the domain order established by the SHAP algorithm and the variable order. 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.  
Figure 3: Relation between predicted and actual cognitive deterioration (**a**) at year 4 and (**b**) at year 5. The predictions were obtained from the LASSOLARS model trained with baseline and first-year change data. Figure 5 shows the results of this approach. Figure 5a demonstrates that the predictions are closer to the diagonal compared to Figure 3, where individual year 4 and year 5 scores were predicted. In Figure 5b, the SHAP diagram confirms the conclusions obtained from previous steps: the cognitive domain remains the most important, with the MoCA variable providing the most information, while the olfactory domain is the least informative. Finally, Figure 5c shows the order of the most relevant variables. Among the cognitive variables, in addition to the two MoCA variables, the SDMT, HVLT-R immediate/total recall, and LNS at baseline stand out. These tests assess specific domains of cognition. To further analyze how the information provided by different variables differs across years, Figure 6 represents the most relevant variables, ordered by their coefficient, for each of the prediction targets analyzed. Year 4 appears on the left-hand side, year 5 on the right-hand side, and the mean in the middle. Lines connect the same variable between year 4 and the mean, and between year 5 and the mean, with different colors corresponding to different domains. Dashed lines indicate that the corresponding variable is significant in only two of the three target predictions. According to the figure, the MoCA variables are \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Year 4** & **Year 5** & **Mean** \\ \hline MAE & 1.55 \(\pm\) 0.23 & 1.56 \(\pm\) 0.19 & 1.26 \(\pm\) 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean absolute error (MAE) of the three prediction targets using the LASSO algorithm trained with baseline and first-year change data. Figure 5: Mid-term cognitive deterioration (mean between year 4 and year 5) predicted with the LASSO model. Figure (**a**): the relation between predicted and actual values; Figure (**b**): the importance of domain by SHAP value; and Figure (**c**): the most relevant variables (\(\Delta\) indicates the first-year change and all the others are baseline values). 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.  
consistently the most important across all prediction targets, with little change between them. Additionally, when variables are relevant for predicting both years 4 and 5, their relevance tends to increase when the target is the mean. Conversely, when a variable is only relevant in two of the three years, its relevance appears to decrease when predicting the mean target.