# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 4 Materials and Methods
The merged feature segments are again subjected to classification. Table 3 shows the performance of classifiers on the primary and derived features segments independently along with all feature segments. By merging the similar feature segments together, it is observed that the decision forests, viz., Random Forest, SysFor and ForestPA shows distinct and the better result both in primary and derived features. Not only that, the decision forests also show best accuracy rate by merging and classifying combined primary and derived features. Though it is clearly visible that the decision forests are far better approach for Parkinson's detection, yet it is difficult to identify the best decision forests among three. In one hand, the SysFor shows better result for primary and derived features but for the entire feature segments with 752 features the ForestPA classifier outperforms other decision forests. This is the reason for which a second stage test is essential to identify the most suitable decision forest along with an ideal acoustic feature segment. At the second stage of analysis and training and testing split on dataset has been conducted. Instead of a traditional way of splitting the underlying data into training and testing groups, a dynamic way of training and testing instances split has been conducted. Moreover, the performance of the decision forests is also observed with different tree sizes in the forest. This second stage analysis, which is the theme of this article has been presented in Figure 1.  
From the literature review, it is revealed that most of the modern PD detection techniques rely on Random Forest as a base learner. However, it is also observed from Table 3 that both the SysFor and ForestPA are a better choice than the Random Forest. According to [PERSON] et al., (2016) [33] and [PERSON] et al. [65], the performance of Random forest decreases with the additional deployment of number of trees in the forest beyond the required numbers. The performance of Random Forest always increases up to a certain number of trees [33]. Afterwards, the deployment of additional trees only increases the computational cost of the classification process [65]. Therefore, controlling the number of decision trees in the forest would improve the time complexity of the entire testing and training process with realization of optimum detection accuracy. In this section, an effort has been made to identify the minimum number of trees required to achieve highest detection accuracy. Instead of leaving the decision forests by their own, the number of tree formation is controlled in each iteration of training and test process. This process continues till the maximum accuracy has been realized with the formation of minimum number of trees. In the meantime, the process will also suggest the ideal training and testing instance split for a dataset. The algorithm for the identifications of perfect training and testing \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifiers** & **Primary Feature Segments** &
\begin{tabular}{c} **Derived** \\ **Feature Segments** \\ \end{tabular} & **All Feature Segments** \\ \hline kNN & 70.24 & 77.78 & 75.79 \\ C4.5 & 77.38 & 74.21 & 78.57 \\ Logistic & 69.44 & 70.24 & 72.62 \\ BPNN & 72.62 & 79.76 & 82.14 \\ NB & 61.91 & 73.02 & 75.00 \\ OneR & 75.00 & 72.22 & 72.22 \\ RF & 79.76 & 81.75 & 80.95 \\ SysFor & 82.94 & 82.54 & 80.95 \\ ForestPA & 77.38 & 77.78 & 82.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The detection accuracy of supervised classifiers on primary and derived features of Istanbul acoustic dataset.