## 7 Conclusions  
The paper presented several solutions towards automating medical data transformation and tests on both natural and artificially created datasets. Selected algorithms used for diagnosis were implemented, tested and had their performance compared in this paper, with their parameters also investigated, making it easier to choose an algorithm better suited for a specific case. Whereas TRSVM may be better suited for smaller datasets with a high amount of dimensions, TWNFI performs better on larger ones, and does not have the performance problem TRSVM does.  
Since it appears that different datasets yield different accuracy rates across all methods, unanswered questions remain about transforming them to improve algorithm accuracy. While the subsets used in our research are all balanced with the exception of instances where the entire datasets are being used, it does very little to improve the performance of the methods. To improve overall algorithm performance, additional factors that influence accuracy need to be identified.  
Despite the scarcity of medical notes, an NLP parser was created, enabling transformation from text to table format, in an attempt to combat the problem of heterogeneity. The parser combines several NLP techniques, such as NER, rule-based recognition and word vectors to extract the necessary values from the text and fill an entry with them in accordance with a schema specified in a file. Although its results are not perfect, it still leaves room for improvement on natural datasets and actual patient notes. Another possible improvement would be modifying the negation detection in the method to make the recognition of Boolean values more effective.  
While the experimental unit conversion method has been shown to work in cases with a lot of known columns that are alike in both datasets being merged, it became a lot less reliable when such columns were fewer, or when it was combined with other data transformation methods. One question regarding the unit conversion method would be its modification to allow different parameters per individual column, due to testing results showing different parameters resulting in optimal values for different columns.  
This modification, however, would also involve solving the problem of picking optimal parameters for such approach.  
The research performed in turn puts more questions up for discussion and further research, such as multicollinearity and ways to detect it, as well as the possibility of an image classifier, and possible applications of polynomial regression to transform data. In addition to that, the diagnosis methods could benefit from cross-validation, since it would help even more to determine optimal parameters, These problems, as well as many other related ones, should be further explored and solved, to continue where this research has left off. Though the ideas described in the paper have been proven to work on the presented datasets, they have quite a few drawbacks, leaving space for future research.  
Conceptualization, E.V. and A.P.-M.; methodology, E.V. and A.P.-M.; validation, A.P.-M. and W.M.; formal analysis, E.V., A.P.-M. and W.M.; investigation, E.V.; resources, E.V.; data curation, E.V.; writing--original draft preparation, E.V., A.P.-M. and W.M.; writing--review and editing, W.M.; visualization, A.P.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.