## 3 Methodology
### Exploratory Data Analysis
#### 3.3.1 Target Class Selection  
Given that the participants of the study were invited to complete both the MMSE and the MoCA cognitive assessments, before and after using the COGNIPLAT platform, there are more than one candidate variables that could be used as the target class. Aiming to select one of these two assessments, the criterion that was most influential had to do with the distribution of scores across the scale of cognitive performance for the MMSE (Figure 3a) and the MoCA (Figure 3b). Both assessments have a similar value range between 1 and 30, however, the cutoff scores of the different cognitive levels differ significantly for each assessment type. This is important as it affects the difficulty to distinguish a subject between the cognitive classes.  
As initially demonstrated by [PERSON] [6], the ranges between the cognitive levels are much less discrete in the MMSE assessment compared to the MoCA assessment. Other researchers confirmed also that the MoCA assessment presents a much better sensitivity in distinguishing subjects with MCI compared to the MMSE due to the fact that often subjects are achieving higher scores in the latter assessment [35]. Finally, normative data for the Greek population are available for the MoCA scale but not for the MMSE.  
Therefore, in this study the MoCA assessment was selected. In particular, the test performed before using the COGNIPLAT platform was chosen due to the following reasons. Firstly, because the two tests were performed in a relatively short period of time it allowed subjects to score better in the latter one due to repetition. Secondly, even with a moderate  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{6}{c}{**Descriptive Statistics**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Scher**} & \multicolumn{1}{c}{**Coeff.**} & \multirow{2}{*}{**Kurtosis**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Median**} & \multirow{2}{*}{**Skewness**} & \multirow{2}{*}{**Variance**} \\  & & **of variation** & & & & & \\ \hline total\_gr\_in\_gs & 12.730 & 0.513 & \(-\)0.044 & 24.798 & 8 & 0.347 & 162.060 \\ total\_success\_rounds\_in\_session & 4.082 & 0.610 & \(-\)1.507 & 6.686 & 4 & 0.032 & 16.666 \\ total\_win\_gr\_points\_in\_gs & 95.309 & 0.796 & 0.038 & 119.636 & 59 & 0.982 & 9898.366 \\ avg\_gr\_time\_in\_gs & 16.395 & 0.422 & 4.929 & 38.828 & 8.527 & 1.666 & 268.799 \\ avg\_gr\_time\_win\_gr\_in\_gs & 21.046 & 0.505 & 1.492 & 41.668 & 10.888 & 1.129 & 442.966 \\ rf\_decimal\_100 & 28.731 & 0.579 & \(-\)1.222 & 49.550 & 24.837 & \(-\)0.046 & 825.518 \\ puzzle\_10 & 1.117 & 0.600 & 1.516 & 1.860 & 0.475 & 1.098 & 147.000 \\ maxelimp & 0.633 & 0.623 & \(-\)0.501 & 1.015 & 0.381 & 0.731 & 0.401 \\ analkislimp & 0.459 & 1.016 & \(-\)1.434 & 0.596 & 0.389 & 0.426 & 0.368 \\ caltmp & 0.729 & 0.686 & 0.136 & 1.062 & 0.4961 & 0.726 & 0.532 \\ namingImp & 0.730 & 0.620 & 0.496 & 1.177 & 0.529 & 0.919 & 0.533 \\ soundImp & 1.037 & 0.888 & 13.422 & 1.166 & 0.583 & 2.907 & 1.075 \\ orientImp & 0.561 & 0.548 & \(-\)0.075 & 1.023 & 0.361 & 0.587 & 0.315 \\ langImp & 0.528 & 0.507 & 1.691 & 1.040 & 0.412 & 0.833 & 0.278 \\ logicImp & 0.372 & 0.363 & \(-\)0.327 & 1.025 & 0.143 & 0.620 & 0.138 \\ memorImp & 0.531 & 0.483 & \(-\)0.673 & 1.098 & 0.426 & 0.329 & 0.282 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Descriptive statistics of the game-based engineered features.  
Figure 3: (**a**) Distribution of MMSE scores before (MMSE_PRE) and after (MMSE_POST) the intervention; (**b**) Distribution of MoCA scores before (MOCA_PRE) and after (MOCA_POST) the intervention.  
usage of serious games designed to train cognitive abilities it was expected to have a positive impact on the follow up MoCA test. Thirdly, as shown in Figure 3b, the distribution of scores in the first MoCA assessment (MOCA_PRE) was slightly more homogeneous than the distribution in the second assessment (MOCA_POST).