# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Results
As shown in Table 11, ET sensitivity percentages for the measurement subset at free motion position are clearly lower than the rest and postural measurement subsets. More specifically, only the Trees classifiers achieved high sensitivity rates with only the Bagged trees reaching up to 90%. However, this is not the case for ET specificity, where the majority of algorithms classified as negative the ET negative cases with success rates up to 100% for Quadratic discriminant analysis, Fine Gaussian SVM, Coarse KNN and Boosted trees. Furthermore, regarding the positive PT cases, only 39.06% of them were correctly categorized by the Quadratic SVM classifier while the other classifiers had even lower sensitivity percentages. In a different pattern, the PT specificity rates for the majority of the classifiers are over 90%. with Fine Gaussian SVM and Boosted trees predicting correctly all the negative PT cases. Moreover, and in comparison to the positive PT cases, the NT ones were categorized as positive at high percentages with Fine Gaussian SVM and Boosted trees achieving the highest success rates. Lastly, the specificity rates of NT remained at relatively high levels with the best percentage being that of 77.34% from Linear Discriminant classifier. In general, the accuracy percentages of the classifiers from the free motion measurement subset are lower than the ones extracted from the previous measurement subsets. This is an expected outcome, because, as seen from the aforementioned paragraph, the sensitivity and specificity rates were lower than for the rest and postural measurement subsets. Specifically, Quadratic SVM along with the Tree classifiers achieved the highest accuracy rates reaching up to 68.13%. The sensitivity, specificity and accuracy rates of the measurement subset related to motion while holding an object are summarized at Table 12. By looking at the table, the majority of the classifiers appear to have high ET sensitivity rates up to 100%. The same can be said for the ET specificity and for the Weighted KNN and RUSBoosted trees classifiers. However, it is worth mentioning that these two classifiers with the highest ET specificity have the lowest ET sensitivity as they were unable to categorize a single positive ET case \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{3}{c}{**Sensitivity**} & \multicolumn{3}{c}{**Specificity**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-7}  & **ET** & **PT** & **NT** & **ET** & **PT** & **NT** \\ \hline Fine Tree & 82.81 & 9.38 & 91.59 & 82.71 & 95.06 & 66.41 & 66.62 \\ Medium Tree & 82.81 & 9.38 & 91.59 & 82.71 & 95.06 & 66.41 & 66.62 \\ Coarse Tree & 78.13 & 3.13 & 92.26 & 79.05 & 97.56 & 63.28 & 64.36 \\ Linear Discriminant & 100.00 & 35.94 & 73.53 & 82.96 & 86.61 & 86.72 & 70.71 \\ Quadratic Discriminant & 100.00 & 35.94 & 73.53 & 82.96 & 86.61 & 86.72 & 70.71 \\ Linear SVM & 100.00 & 35.94 & 70.65 & 82.96 & 84.82 & 86.72 & 69.42 \\ Quadratic SVM & 98.44 & 48.44 & 86.68 & 87.50 & 92.73 & 86.72 & 79.13 \\ Cubic SVM & 98.44 & 48.44 & 88.68 & 82.58 & 94.05 & 98.44 & 80.86 \\ Fine Gaussian SVM & 100.00 & 40.63 & 90.53 & 84.45 & 95.24 & 90.63 & 80.40 \\ Coarse Gaussian SVM & 100.00 & 40.63 & 91.91 & 83.26 & 97.02 & 93.75 & 82.62 \\ Fine KNN & 100.00 & 25.00 & 84.56 & 90.48 & 87.50 & 75.00 & 71.36 \\ Medium KNN & 100.00 & 37.50 & 85.72 & 79.98 & 94.94 & 93.75 & 78.47 \\ Coarse KNN & 85.94 & 50.00 & 85.98 & 83.18 & 89.29 & 99.22 & 78.00 \\ Cosine KNN & 100.00 & 37.50 & 87.75 & 85.42 & 94.35 & 86.72 & 79.01 \\ Cubic KNN & 98.44 & 39.06 & 77.32 & 76.33 & 90.48 & 100.00 & 74.37 \\ Weighted KNN & 0.00 & 3.13 & 100.00 & 100.00 & 97.32 & 6.25 & 56.40 \\ Boosted trees & 96.88 & 17.19 & 66.75 & 64.43 & 90.18 & 99.22 & 64.79 \\ Bagged Trees & 98.44 & 37.50 & 76.36 & 76.33 & 89.88 & 99.22 & 73.50 \\ Subspace KNN & 100.00 & 34.38 & 81.17 & 75.74 & 93.45 & 98.44 & 75.23 \\ RUS Boosted trees & 0.00 & 0.00 & 100.00 & 100.00 & 100.00 & 0.00 & 55.72 \\ \hline \hline \end{tabular}
\end{table}