# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 5 Results and Discussion
Not only this, the ForestPA revealed at par result than its peer forests in Baseline and TQWT features. Similarly, in Figure 5 also all the classifiers have identical sensitivity; thus, an inconclusive result about the best decision forest. Both Random Forest and ForestPA have equal sensitivity for MFCC and TQWT derived features and SysFor and ForestPA have equal sensitivity rate for Baseline and VocalFold Features. On the other hand, the SysFor outperforms other decision forests in Baseline, MFCC, and Wavelet feature segments, whereas Random Forests performs well with highest sensitivity in time frequency, Vocal Fold feature segment. Though an indication has been received that the SysFor would be a good approach towards Parkinson's detection with better sensitivity than its peers, more conclusive evidence is expected to justify the claim. Therefore, at the next step the combined primary feature segments (Time Frequency, Baseline and Vocal Fold) and combined derived feature segments (MFCC, TQWT and Wavelet) and the total feature segments are treated separately with the decision forests. The result of decision forests on respective combined feature segments and total feature segments are presented in Tables 8-10. Parkinson and vice versa. Another reason for which the SysFor can be considered as an ideal Parkinson detector is the specificity. With 82.50% specificity, the SysFor classifies control subjects perfectly. Similarly, for derived feature segments, ForestPA reckons the highest ever performance with just nine trees in the forest. Not only that, the ForestPA performs well in precision, sensitivity, fall-out and miss-rate. However, it slips away in specificity. Finally, when all the feature segments are combined and processed through decision forests separately, the ForestPA still retains its position with 94.12% of detection accuracy. The ForestPA also reveals the best result in all performance measures, viz., accuracy, precision, sensitivity, specificity, fall-out and miss-rate. At this point, it is quite clear that the ForestPA is the ideal decision forest for acoustic signal-based Parkinson's detection, but comparing Tables 8 and 9 analogically it is quite evident that the primary feature segments such as time frequency, baseline and vocal fold are the better segment as compared to derived features. With better precision and specificity with lowest fall-out and miss rate the primary feature segments contribute better towards the Parkinson's detection process. Nevertheless, ForestPA proved to be a decent Parkinson's detector with better results in derived features but, at the same time, it also took only seven trees in the case of primary features with satisfactory detection results. Therefore, in a nutshell the primary feature segments are proved to be better features as compared to derived features. At this point it is proved that the proposed approach for decision forest based Parkinson's detection shows convincing results when decision trees are controlled within the forest. However, it is quite interesting to observe the potentiality of the proposed approach with the recent state of the art approaches. In this regard few recent Parkinson's detection approach has been shortlisted and compared. The approaches used for comparison are the deep learning based Convolutional Neural Networks (CNN) Parkinson's detection [52], Random Forest and SMOTE hybrid [23], Minimum Average Maximum Tree (MAMT) approach [55], XGBoost approach [56] and Gaussian Mixture Models (GMM) approach [57]. All these approaches are based on the same dataset and on same evaluation criteria. The comparative analysis has been presented in Table 11.  
From the Table 11 it can be observed that our proposed approach with ForestPA controlled decision trees dynamically proved to be superior Parkinson's detector as compared to other detection scheme. The nine layer of CNN deep learning approach [52] though shows promising result, but the method is not convincing even at per with other machine learning approaches.