## 6 Analysis of Experiment Results
### Results Discussion  
As was obtained, Naive Bayes classifiers performed rather poorly on medical data, with the results for both of classifiers fluctuating around 40-60%, which may be linked to the issue of multicollinearity mentioned in the previous section, and the system's inability to detect it in all of the cases. Though the results appear to be better with feature selection, the classifier as it is not enough to produce a reliably accurate diagnosis for usage in real-life cases.  
In comparison, TWNFI performs poorly on smaller datasets, only reaching an accuracy of 50% on a 100-sample subset, but seems to perform relatively well on larger datasets with proper parameters, achieving the highest accuracy in the series of experiments at 82% for 5000 samples and further confirming the ideas mentioned in [28]. TWNFI's fitting time also decreases with a larger dataset, going from 20,456.25 ms to 12,122.58 ms, which could be attributed to a larger amount of samples allowing the algorithm to pick a more precise set that takes less time to tune, all local sub-models in testing being of relatively the same size, and clustering being only applied to a local area. However, to adjust TWNFI to a specific problem, one would have to pick suitable parameters (like number of samples) for it to both make accurate predictions and perform within a reasonable time limit.  
TR-SVM's showcases better results on smaller datasets, achieving the highest accuracy of 72% among all the algorithms on a subset of 100 samples, but it is impractical to use on larger ones due to large computation times, which reached several days on a subset of 5000 samples, which can take days even on more powerful machines. While the pruning heuristic does offer to decrease some of this time, it sacrifices a certain amount of accuracy in favor of faster execution, with larger values of parameter S decreasing accuracy from around 90% to only 60%, and is still not enough to make a great difference in the case of larger datasets. As a result, it is best suited for the same cases as other SVM: small datasets of high-dimensional data.  
The NLP note parser appears to give tolerable results as of now, but still far from being viable due to the way it is currently implemented, with accuracy per sample varying from 100% to 45%, as well as the lack of available datasets containing patient notes. Its inaccuracies are also partially due to a lack of publicly available data that features medical notes, resulting in a worse recognition quality. In addition to that, while parsing itself only takes approximately 2000-3000 ms, the same cannot be said about the initialization time, which involves loading all the models. As a result, it may be more effective to parse several notes simultaneously.  
The experimental unit conversion works in most cases; however, its effectiveness greatly depends on the parameters such as the number of similar neighbours and the amount of steps. More experiments on different kinds of datasets are required to develop it further. The quality of the conversion also appears to greatly decrease when applied together with other methods, resulting in coefficients almost 1.5 times larger than the target results, such as the amount of days in year being erroneously assumed to be closer to 500 instead of the intended 365.25.