## 5 Discussion  
This work has shown that it is possible to create ML models based on data collected from serious games and transformed to engineered features along with relevant subjective information. These models can be used then to accurately classify whether a subject belongs to the MCI or NC group as attested by the MoCA cognitive test. In this context, a focal point of the research performed was the development of a custom methodology to train such MCI detection models with low bias and variance and to validate the models using established and solid metrics and techniques, while being attentive to maintain high performance in terms of sensitivity and specificity. There are 31 features that originally were defined to train the models from which 15 are related to the games, 14 are related to demographic and health data and 2 are artificial variables used as reference points to filter features with a lower importance than them during the feature selection process. Mixing technology-based and subjective data in order to improve the predictive performance of a cognitive impairment detection model is not  
Figure 14: (**a**) ROC-AUC for the production level models and the manually selected features; (**b**) Precision–recall/sensitivity for the production level models and the manually selected features. Figure 15: (**a**) ROC-AUC for the production level models and the automatically selected features; (**b**) Precision–recall/sensitivity for the production level models and the automatically selected features. unprecedented, as a similar approach has been demonstrated in other studies [56]. The inclusion of features that represent demographics, health and lifestyle cater for improving not only the performance but also the generality of the prediction. As a matter of fact, such factors are taken into account also when traditional assessments are used to evaluate cognitive impairment [57]. For example, in MoCA assessment, a score adjustment is allowed depending on the education level of the subjects [6; 30]. For the production model trained with the manually selected features and the Support Vector Classifier integrating all the optimization techniques and in the context of the pipeline method an accuracy of 91.79%, a sensitivity of 93.20% and a specificity of 90% were achieved. On the other hand, for the production model trained with the more verbose set of automatically selected features using the Gaussian Naive Bayes algorithm under the pipeline context, the corresponding evaluation metrics were 92.14%, 93.4% and 90%. However, for the specificity metric a higher standard deviation is observed which is due to the fact that for the creation of the production model the testing dataset does not undergo the oversampling process which is now part of the pipeline and happens later in the workflow. Consequently, the true negative values are fewer and therefore small errors of the model lead to a large variation. Both feature selection strategies lead to models with roughly equal performance however the model with the manually selected features is 18% more compact. This model includes 9 features with 5 of them representing game data and 4 of them representing subjective data. The COGNIPLAT game suite includes games which target cognitive functions that are linked to the assessment of MCI. From the features that have been selected in the machine learning models it is observed that the games that are associated with the cognitive areas of short-term memory, visual memory, episodic memory, spatio-temporal orientation and executive functions are the most important predictors of cognitive impairments. This is reasonable since the design of the corresponding games focused on several occasions on porting typical cognitive assessments in a gamified environment. For example, the _Orientation_ game was inspired by [PERSON]'s Picture Arrangement Subset [58] which is used to assess perception and problem-solving cognitive operations that are associated with spatio-temporal orientation.