# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 4 Materials and Methods
The classifiers are used to classify each feature segment stated in Table 1. The identification features are dropped from the dataset as these features are no direct link with the primary and derived features. The 10-fold cross-validation approach has been used to validate the performance of the classifiers. The validation process applied to each feature segment separately on primary and derived feature segments. This will also help to identify the prominent feature segments' contribution towards the Parkinson's detection. There exist many performance measures for decision forests, viz., detection accuracy, mis classification rate, precision, false alarm rate. Specifically, for Random Forest, test error is estimated based on out-of-bag error as Random Forest works on bootstrap sampling. According to a recent study by [PERSON] et al. (2018), the OOB revealed overestimated in balanced settings and in settings with few samples. In our case the dataset has only 252 instances and the training and testing split of instances are selected ranging from 30-70% to 80-20%. In the case of bootstrap sample by Random Forest, the size of training set may be reduced further, which may seriously impact OOB error estimates. Therefore, other performance measures like detection accuracy, precision has been considered for evaluating decision forests as [PERSON]'s detector. Moreover, since two more decision forests ForestPA and SysFor has been taken into consideration; the accuracy measure has been given preference over error based on OOB. The detection accuracy of decision forests has been estimated for comparison and analysis is presented below. \[Accuracy\;(\%)=\;\frac{(TP+TN)}{(TP+TN+FP+FN)}*100 \tag{11}\]  
where _TP_ = True Positive instances, _TN_ = True Negative instances, _FP_ = False Positive instances and _FN_ = False Negative instances. The output of the classifiers of each feature segment is presented in Table 2.  
Classifying each feature segment shows satisfying detection accuracy for all classifiers. However, for a specific feature segment, a specific classifier shows highest accuracy rate. For an instance, the Random Forest shows better classification accuracy for Time Frequency, MFCC and TQWT feature segments. Similarly, the Baseline and Wavelet features are classified nicely by BPNN and OneR, respectively. On the other hand, using the vocal fold features, the SysFor decision forest segregates PD subjects from controls with 79.37% accuracy. It is evident that decision forests, viz., Random Forest and SysFor are responsible for the best acquired result for almost all feature segments due to the ensemble of decision \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Primary**} & \multicolumn{3}{c}{**Derived**} \\ \cline{2-7}
**Classifiers** & \multicolumn{3}{c}{**Feature Segments**} & \multicolumn{3}{c}{**Feature Segments**} \\ \cline{2-7}  & **Time** & **Baseline** & **Vocal Fold** & **MFCC** & **Wavelet** & **TQWT** \\ \hline kNN & 75.79 & 73.02 & 71.03 & 75.00 & 68.25 & 78.18 \\ C4.5 & 73.02 & 75.00 & 77.78 & 71.83 & 65.48 & 72.62 \\ Logistic & 76.19 & 75.00 & 75.40 & 72.62 & 58.73 & 69.44 \\ BPNN & 75.00 & 78.97 & 67.46 & 78.97 & 69.05 & 78.97 \\ Naïve Bayes & 63.10 & 51.19 & 73.81 & 60.32 & 69.05 & 74.60 \\ OneR & 67.06 & 73.02 & 78.57 & 76.98 & 76.19 & 70.24 \\ Random Forest & 80.56 & 76.19 & 78.97 & 82.14 & 75.40 & 79.76 \\ SysFor & 74.60 & 73.81 & 79.37 & 75.79 & 71.83 & 78.18 \\ ForestPA & 77.78 & 75.40 & 78.97 & 80.56 & 75.79 & 78.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The detection accuracy of supervised classifiers on each feature segments of Istanbul acoustic dataset. trees in the forest. However, it does not imply that the decision forests are the best option as compared to other supervised classification techniques. Therefore, to come across a valid conclusion, the primary features viz. time frequency, baseline and vocal folds and the derived features, viz., MFCC, TQWT and wavelets are merged separately.