## 3 Methodology
### Exploratory Data Analysis
#### 3.3.2 Preprocessing  
Missing Values Management  
In the case of our dataset, the only entries with missing values were a few entries representing game rounds that terminated due to application exceptions. Since these rounds were only a few and they had most of their fields missing the decision was to discard and not include them in the schema migration following the tuple ignoring technique [36]. Management of Outliers  
Outliers apply only to values of fields that represent in-game data and not to fields that are related to the demographics and other questionnaires that the subjects completed and cannot deviate from predefined values. Given that the size of the dataset is relatively limited, removing entries that contain outlier values in one or more fields is probably not the best option. On the other hand, leaving those values as-is could potentially affect the results in the process of scaling, depending on the algorithm that will be selected to apply. Ideally, when a game session resembles an assessment, it provides a specific number of game rounds, in a specific order, with a specific difficulty progression. The COGNIPLAT platform which was used for data gathering serves a dual goal both for cognitive assessment and for exercising cognitive functions of the elderly. As a consequence, the level of difficulty was customizable allowing the application or the caregiver to adjust it in order to meet the capabilities of each subject. On the other hand, the game performance in terms of points won in a game round is directly related to the game difficulty level. Additionally, the subjects had the option to repeat a level for several times. These characteristics resulted in some game sessions with distinctly differentiated scores. The way the issue of outliers was addressed was by value replacement and by applying the Winsorization technique [37]. The technique was implemented to calculate new values based on the following strategy. If the feature represented a total, for example the total points gathered in the successful game rounds of a session, and the value for this feature in an entry was too high, then it was replaced with the maximum value (Q3 + 1.5*IQR) of the distribution of the feature. Respectively the low-end outlier values of an entry for a feature representing a total value, were replaced by the minimum value (Q1 + 1.5*IQR) of the distribution. On the other hand, for features that represent an average value, for example the average completion time of a successful game round, the outlier values were replaced by the median value of their distribution. Discretization  
Although discretization by binning is a relatively simple data transformation, in our methodology binning of feature values to higher levels is an essential step and it has been applied for the target class and for features derived from in-game data with continuous values. Firstly, discretization was applied to the target class, which represents the MoCA scores recorded before the game sessions. The implementation is affected by the type of the target class field because it defines what kind of ML algorithms, between regression and classification, can be used to train the model. Additionally, this affects the way a prediction is interpreted, since an answer in the MoCA range of results would give a specific estimate while the objective is to get a broader estimate of the cognitive level of the subject as a classification between two cognition levels: normal cognition (NC) and mild cognitive impairment (MCI). Secondly, before moving to feature selection, some normalization method needs to be applied to avoid the outweighing of features with low value ranges. In the case of the target class, the exact range of each bin is known beforehand, which happens to be the MoCA cutoff scores of each cognitive level.