# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 5 Results and Discussion  
From the 10-fold cross-validation the result was not determined to get the best decision forest and the best feature segment for Parkinson's detection. Therefore, a dynamic training and testing split of instances with change in number of trees has been carried out and, in the meanwhile, three distinct Parkinson's detection schemes has also been proposed using decision forests. The result of the proposed approaches has been obtained in two different ways. At first, the detection accuracy of decision forests has been observed with a change in the number of decision trees for each block of the training-testing split. Secondly, the best training-testing split has been proposed as the PDS with the smallest number of decision tree formation for getting the highest detection accuracy with lowest error rate. The analysis has been conducted on both the acoustic signal datasets mentioned in Section 4. The performance of decision forests with incremental number of decision trees and training testing splits on Istanbul acoustic signal dataset are outlined in Figure 2. At each change in training and testing split and number of decision trees, the performance of decision forests varies. From Figure 2, it is observed that the ForestPA and SysFor require lesser number of trees to achieve highest accuracy. With just 30% of training instances, both ForestPA and SysFor show the detection accuracy between 80%-90%. However, with the increase in number of trees the Random Forest shows the superior result than its peer. A similar result is observed with 40% training instances. However, with increase in training instances the SysFor evolves as a leading decision forest with very little number of trees. With 60% training instances the SysFor infers a visible performance improvement over other decision forests. It should be noted that all the decision forests do not require a significant number of trees to touch the highest accuracy mark. Therefore, during the classification process, the number of trees in the forests should be controlled rather than leaving it to the environment. A similar kind of observation is also noticed for the Spanish acoustic dataset presented in Figure 3. Figure 3 shows the detection accuracy derived from the decision forest with a change in trees for various training and testing split. It can be seen from the figure that, both the ForestPA and SysFor shows better detection accuracy for each increase in tree size. The highest detection accuracy is also observed within just 20 decision trees. This shows the default number of decision trees for SysFor is not reliable. With only below 10 decision trees, the SysFor shows better results for all training and testing split in terms of detection accuracy. Figure 2: Detection accuracy with respect to change in number of trees in the decision forests observed for each training and testing split on Istanbul acoustic dataset. Further, the 75-25% training and testing split reveal the highest detection accuracy for all the decision forest. Though SysFor seems to be ahead of ForestPA and Random Forest for almost all the training-testing split, all the decision forest reveals the highest accuracy for 75-25% split. To come across a concrete conclusion, the decision forests are further analyzed for the highest detection accuracy with minimum number of trees (t), precision (Prc), sensitivity (Sen), specificity (Spc), fall-out, and miss rate. The result obtained by second level of analysis for all the decision forests has been presented in Tables 5-7. Figure 3: Detection accuracy with respect to change in number of trees in the decision forests observed for each training and testing split on Spanish acoustic dataset. Table 5-7 does not really compare the decision forests algorithms, instead, it shows the performance of the decision forests on various feature segments of acoustic datasets provided by the Department of Neurology in Cerrahpasa, Istanbul. However, the performance comparison of the classifier can be analyzed for each feature segment.