## 6 Conclusions  
By using the LIME explainability method for the purpose of extracting the relevant features and applying the features attention mechanism to them, we managed to increase the accuracy of the final model by approximately 20% from 60% in the first run to above 80% after the explainability-attention refinement. Thus, we highlighted the importance of extracting only the relevant features from the initial dataset. We managed to apply this method to obtain good predictions, even if the size of the dataset was small, with only 100 real patients in the first dataset and 138 real patients in the second dataset participating in this study.  
We compared our novel model, including dense classification layers, with the standard deep neural network that is embedded into it. The results are shown in Tables 2 and 3, where the performance metrics before using the developed method are shown along with the ones computed for the dense neural network embedded in our model. There were two significant improvements over the standard deep learning approaches. One is based on the application of LIME to emphasize the feature importance, and the second is to use this information as a feedback signal for the attention mechanism.  
Initially, the model was designed for a small dataset; later, it was tested on a dataset with a larger volume of information, as well as in the multi-class case, where it behaved very well.  
Small datasets have unique characteristics and constraints that significantly influence the choice of methods and tools used for analysis and modeling. For instance, in small datasets, there is a higher risk of overfitting when using complex models, as these models might adapt too closely to the limited data available, failing to generalize well to new, unseen data.  
At the same time, as a final conclusion, we would like to specify the fact that we were not focused on the volume of the dataset, but whether the developed model would be appropriate or bring a positive contribution to automatic learning. We aimed to analyze the performance of the model and to check whether, by using it, we will be able to improve the predictions. The fact that we followed two sets of data from the clinical field, but from different areas of medicine, furthermore proves the versability and the flexibility of the method in executing different tasks, e.g., binary and multi-class classification.  
Conceptualization, D.M.O. and C.C.S.; methodology, C.C.S., D.M.O., G.V.C. and C.I.; software, F.C.; validation, C.C.S., D.M.O. and G.V.C.; formal analysis, C.C.S., C.I. and D.M.O.; investigation, C.C.S. and D.M.O.; resources, C.C.S. and G.V.C.; data curation, C.C.S., D.M.O. and F.C.; writing--original draft preparation, F.C. and D.M.O.; writing--review and editing, C.C.S. and F.C.; visualization, G.V.C. and C.I.; supervision, D.M.O. and C.I.; project administration, D.M.O. and F.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Clinical Emergency County Hospital, Timisoara, No. 104/15 December 2016.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.