# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{4}{c}{**Sensitivity**} & \multicolumn{4}{c}{**Specificity**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-7}  & **ET** & **PT** & **NT** & **ET** & **PT** & **NT** \\ \hline Fine Tree & 98.44 & 54.69 & 58.23 & 83.53 & 81.36 & 90.63 & 68.15 \\ Medium Tree & 98.44 & 54.69 & 58.23 & 83.53 & 81.36 & 90.63 & 68.15 \\ Coarse Tree & 98.44 & 48.44 & 57.48 & 78.55 & 83.47 & 90.63 & 65.83 \\ Linear Discriminant & 98.44 & 53.13 & 64.24 & 84.41 & 81.20 & 96.09 & 71.59 \\ Quadratic Discriminant & 100.00 & 81.25 & 55.11 & 100.00 & 68.43 & 90.63 & 70.41 \\ Linear SVM & 96.88 & 53.13 & 48.15 & 88.51 & 66.30 & 93.75 & 61.68 \\ Quadratic SVM & 100.00 & 62.50 & 54.27 & 94.14 & 70.07 & 90.63 & 67.48 \\ Cubic SVM & 100.00 & 71.88 & 50.62 & 96.27 & 66.61 & 91.41 & 66.83 \\ Fine Gaussian SVM & 100.00 & 70.31 & 58.03 & 100.00 & 70.73 & 85.16 & 69.83 \\ Medium Gaussian SVM & 100.00 & 54.69 & 56.97 & 90.51 & 73.85 & 91.41 & 68.08 \\ Coarse Gaussian SVM & 67.19 & 50.00 & 61.16 & 91.51 & 69.27 & 85.16 & 62.95 \\ Fine KNN & 100.00 & 76.56 & 59.18 & 98.90 & 72.22 & 89.84 & 72.81 \\ Medium KNN & 100.00 & 51.56 & 60.21 & 88.51 & 76.82 & 92.19 & 69.32 \\ Coarse KNN & 0.00 & 18.75 & 82.69 & 100.00 & 79.76 & 21.88 & 50.30 \\ Cosine KNN & 100.00 & 43.75 & 55.67 & 81.85 & 77.08 & 93.75 & 65.29 \\ Cubic KNN & 100.00 & 46.88 & 59.84 & 87.01 & 77.13 & 91.41 & 68.07 \\ Weighted KNN & 100.00 & 51.56 & 61.36 & 90.51 & 76.65 & 90.63 & 69.98 \\ Boosted trees & 0.00 & 0.00 & 100.00 & 100.00 & 100.00 & 0.00 & 52.89 \\ Bagged Trees & 100.00 & 65.63 & 60.36 & 93.57 & 75.47 & 91.41 & 71.86 \\ Subspace KNN & 100.00 & 60.94 & 58.32 & 91.20 & 75.17 & 89.84 & 69.08 \\ RUS Boosted trees & 100.00 & 54.69 & 58.18 & 84.31 & 82.41 & 86.72 & 68.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average sensitivity, specificity and accuracy percentages of classification methods for proofual position measurements. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{4}{c}{**Sensitivity**} & \multicolumn{4}{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 & 50.00 & 37.50 & 65.02 & 65.36 & 86.98 & 77.34 & 53.16 \\ Quadratic Discriminant & 50.00 & 4.69 & 99.26 & 100.00 & 89.48 & 40.63 & 60.28 \\ Linear SVM & 50.00 & 32.81 & 62.88 & 67.49 & 84.98 & 73.44 & 51.10 \\ Quadratic SVM & 57.81 & 39.06 & 91.59 & 93.01 & 88.48 & 65.63 & 68.13 \\ Cubic SVM & 57.81 & 15.63 & 84.84 & 92.34 & 80.30 & 60.16 & 57.95 \\ Fine Gaussian SVM & 50.00 & 0.00 & 100.00 & 100.00 & 100.00 & 25.00 & 59.52 \\ Medium Gaussian SVM & 65.63 & 28.13 & 73.58 & 94.95 & 85.86 & 49.22 & 58.12 \\ Coarse Gaussian SVM & 25.00 & 31.25 & 84.84 & 91.50 & 85.80 & 45.31 & 53.47 \\ Fine KNN & 65.63 & 7.81 & 84.12 & 93.82 & 95.71 & 36.72 & 57.44 \\ Medium KNN & 65.63 & 28.13 & 75.53 & 90.89 & 92.24 & 48.44 & 59.26 \\ Coarse KNN & 0.00 & 0.00 & 98.81 & 100.00 & 99.32 & 0.00 & 44.48 \\ Cosine KNN & 65.63 & 32.81 & 64.72 & 82.46 & 90.20 & 56.25 & 55.90 \\ Cubic KNN & 65.63 & 28.13 & 70.90 & 88.89 & 91.14 & 48.44 & 56.93 \\ Weighted KNN & 65.63 & 28.13 & 76.53 & 91.50 & 92.74 & 47.66 & 59.69 \\ Boosted trees & 0.00 & 0.00 & 100.00 & 100.00 & 100.00 & 0.00 & 44.32 \\ Bagged Trees & 90.63 & 12.50 & 80.29 & 78.85 & 93.56 & 69.53 & 63.53 \\ Subspace KNN & 71.88 & 29.69 & 70.55 & 96.82 & 82.06 & 54.69 & 59.48 \\ RUS Boosted trees & 57.81 & 9.38 & 93.06 & 86.25 & 90.56 & 57.81 & 60.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average sensitivity, specificity and accuracy percentages of classification methods for proofation measurements.