# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Results
Moreover, Cubic SVM, as observed from the Table 9, appears to have high sensitivity and specificity rates, except the occasion of the PT sensitivity which is lower than 45%. Table 10 shows the average sensitivity, specificity and accuracy percentages for the measurement subset at postural position. Regarding the classification of ET, we can observe that 18 out of 21 classifiers were able to accurately predict the positive ET cases with a success rate over 96.88%. In parallel, all the classifiers predicted correctly the negative cases as negative with percentages over 78.55%. For the subjects with PT, the sensitivities of the classifiers at rest positions are clearly lower than the ones for the ET subjects. However, Quadratic discriminant classifier predicted correctly the 81.25% of positive cases of PT, although the specificity of the classifier for PT instances fell below 69%. On the contrary, Boosted trees classifier managed to identify correctly all the negative cases of PT. Lastly, all the classifiers showed difficulty in predicting all NT positive cases, except the Boosted trees which achieved 100% sensitivity of NT cases despite the inability of the same classifier to predict any of the negative NT cases. Specifically, the majority of the tested classifiers categorized the negative NT cases as negative at a percentage around 90%. Regarding the average accuracy for each of the 21 classifiers for the postural measurement subset, Fine KNN classifier stands out as the most successful in terms of prediction rate with 72.81%. Furthermore, this classifier, as observed from Table 10, appears to have high sensitivity and specificity rates, except the occasion of the NT sensitivity which is lower than 60%. It must be mentioned that the accuracy rates of the most of the classifiers are close to 70% and the accuracy of Fine KNN. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \multirow{2}{*}{**Classifier**} & \multicolumn{3}{c}{**Sensitivity**} & \multicolumn{3}{c}{**Specificity**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{6-7}  & **ET** & **PT** & **NT** & & **ET** & **PT** & **NT** \\ \hline Fine Tree & 90.63 & 51.56 & 46.52 & 85.59 & 73.92 & 81.25 & 60.31 \\ Medium Tree & 90.63 & 51.56 & 46.52 & 85.59 & 73.92 & 81.25 & 60.31 \\ Coarse Tree & 89.06 & 57.81 & 35.53 & 90.62 & 65.43 & 79.69 & 57.30 \\ Linear discriminant & 89.06 & 59.38 & 64.74 & 83.64 & 83.53 & 88.28 & 69.88 \\ Quadratic discriminant & 85.94 & 48.44 & 77.24 & 92.57 & 87.10 & 75.00 & 71.60 \\ Linear SVM & 96.88 & 39.06 & 76.01 & 80.98 & 94.64 & 80.47 & 71.54 \\ Quadratic SVM & 96.88 & 39.06 & 75.60 & 85.86 & 91.51 & 78.13 & 71.56 \\ Cubic SVM & 98.44 & 43.75 & 80.59 & 87.81 & 95.24 & 77.34 & 75.17 \\ Fine Gaussian SVM & 92.19 & 64.06 & 59.66 & 97.30 & 75.43 & 81.25 & 69.73 \\ Medium Gaussian SVM & 92.19 & 65.63 & 50.41 & 88.67 & 77.73 & 83.59 & 66.78 \\ Coarse Gaussian SVM & 48.44 & 32.81 & 64.29 & 82.74 & 75.97 & 64.84 & 50.98 \\ Fine KNN & 98.44 & 53.13 & 57.55 & 84.75 & 82.66 & 84.38 & 67.64 \\ Medium KNN & 84.38 & 62.50 & 47.02 & 88.84 & 72.38 & 82.03 & 62.08 \\ Coarse KNN & 0.00 & 0.00 & 100.00 & 100.00 & 98.21 & 2.34 & 43.53 \\ Cosine KNN & 93.75 & 53.13 & 40.75 & 86.42 & 67.29 & 86.72 & 59.26 \\ Cubic KNN & 79.69 & 62.50 & 47.25 & 90.19 & 69.00 & 82.03 & 60.66 \\ Weighted KNN & 96.88 & 62.50 & 50.14 & 90.70 & 76.54 & 82.81 & 66.86 \\ Boosted trees & 0.00 & 0.00 & 100.00 & 100.00 & 100.00 & 0.00 & 43.53 \\ Bagged Trees & 92.19 & 48.44 & 57.10 & 90.03 & 81.55 & 73.44 & 64.79 \\ Subspace KNN & 92.19 & 29.69 & 41.71 & 75.58 & 78.17 & 74.22 & 52.43 \\ RUS Boosted trees & 96.88 & 59.38 & 46.02 & 77.69 & 80.20 & 89.84 & 64.00 \\ \hline \end{tabular}
\end{table} 
Table 9: Average sensitivity, specificity and accuracy percentages of classification methods for rest position measurements.