# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Results
Furthermore, the 96.5% accuracy provided by the Cubic SVM classifier in \(F_{4}\) stands out among the rest in terms of accuracy while success rates over 90% were provided for other measurements in the aforementioned arm positions (\(M_{4}\)--Cubic SVM, \(T_{2}\)--Quadratic SVM, \(T_{4}\)). In the object-holding arm position, the classification of new examples was more successful than of the free motion position, because as the hand moves, the strength of the muscles gradually weakens causing an increase in tremor amplitude. In the midst of the extracted success rates are the tremor identification algorithms based on free movement positions. Although at these positions the hand muscles are similarly weakened due to the existence of gravitational pull and movement, they do not fade in free-motion as much as in the holding-object position and the characteristics of each tremor become less distinct. However, accuracy of around 90% can be observed for Cubic SVM and Linear discriminant through the free motion measurements of the metacarpal and lower forearm. In order to further analyze the classifiers' performance, the training times and prediction speeds have been extracted and are presented in Table 8. As observed, the lowest training time was achieved by RUSBoosted trees classifier with 0.45 s during \(I_{1}\) measurement, along with the high prediction speed of 4800 obj./s. However, RUSBoosted trees managed to classify correctly 67.2% of the total examples, a relatively low rate comparing to the rest of the classifiers. Similarly, Weighted KNN achieved high prediction speed (5700 obj./s) with low training time (0.55 s) but in expense of low accuracy rate, that of 62.1%. It is noteworthy that the highest training time was over 7 s from Bagged trees during \(I_{1}\), the same measurement where the lowest training time was achieved. In \(M_{2}\), Quadratic and Cubic SVM classifiers achieved the highest accuracy rates while combining relatively low training time and high prediction speed. Finally, Linear SVM must be highlighted as the classifier that during \(F_{4}\) measurement was characterized by high accuracy, training time and prediction speed with 92.0%, 1.17 s and 4200 obj./s, respectively. Tables 9-12 summarize the average sensitivity, specificity and accuracy percentages of each measurement subset, in order to further assess the presented classification results concerning the performance of the 21 selected classifiers. Table 9 presents the average values of sensitivity, specificity and accuracy percentages for each of the 21 tested classifiers for the rest measurement subsets. As observed from the Table, the majority of the algorithms were able to accurately classify the positive cases of ET with high success. Especially, all but one SVM classifier reached very high sensitivity percentages and even near 99% for the case of Cubic SVM. In parallel, SVM algorithms managed to classify most of the negative cases of ET as negative, although similar to the sensitivity, for ET cases most of the classifiers achieved high specificity percentages with Medium KNN and Boosted trees classifiers predicting correctly all the negative ET cases as negative. Unlike the ET cases, for the PT we can discern the difficulty of the classifiers to correctly predict the positive cases with the maximum sensitivity percentage being that of Medium Gaussian SVM classifier with 65.63%. On the contrary, the specificity rates are highly increased and reach up to 100% for Boosted trees. Lastly, regarding the sensitivity rates of NT, Boosted trees and Medium KNN classifiers achieved the highest with 100% while for the specificity of NT, RUS Boosted trees achieved a percentage of 89.84%. Regarding the average accuracy for each of the 21 classifiers for the rest measurement subsets, the Cubic SVM classifier provided the most successful prediction rate with 75.17%, while Linear, Quadratic and Fine Gaussian SVM reached relatively high percentages near 70%.