# Machine Learning Methods with Decision Forests for Parkinson's Detection
###### Abstract
However, there is a need of describing the efficiency of decision forests in terms of tree density in the forest and a dynamic training, and testing split. In this article, three distinct Parkinson's detection schemes have been proposed using Random Forest, SysFor, and ForestPA. The proposed schemes undertake varying tree density with incremental training and testing samples to detect the presence of [PERSON]'s. The detection capability of decision forests is measured with the lowest number of decision trees they form during the process of classification to achieve maximum detection efficiency. A challenge lies in data science, especially in medical datasets is about the ideal training and testing split of underlying data. The ideal training and testing split are still debatable. Many articles related to Parkinson Detection carried out the evaluation using 60-40% splitting of training and testing sets, whereas many others claim the 80-20% is ideal. In view of this contradiction, this article finds a suitable training and testing instances split of acoustic signal data for Parkinson's detection. Another challenge that the Parkinson detection approaches face while using acoustic signal is a selection of prominent voice-based features. In other words, the acoustic signal contains many voice-based features. For instance, baseline features of voice signal consist of Pitch period entropy (PPE) and Detrended fluctuation analysis (DFA) and various jitter, shimmer and Harmonic to Noise Ration attributes of the voice signal. Similarly, time frequency features comprise of Intensity parameters, Formant frequency and Bandwidth. Intensity represents the loudness of speech signal which is related to the subglottis pressure of the airflow that depends on the tension and vibration of the vocal folds [9; 29]. On the other hand, many derived voice-based feature segments are realized, viz., Mel-Frequency Cepstral Coefficients (MFCC) [30; 31], Wavelet [32] and Tunable Q-Factor wavelet transform (TQWT) [9] respectively for identification [PERSON]'s disease. These features are extracted from the acoustic signal using specialized software. Therefore, in this context, while analyzing decision forests algorithms an effort has been made to identification of the suitable feature segment that promotes detection accuracy of [PERSON]'s detection. The contribution of this article is as follows:  
* Development of three distinct Parkinson's detection systems using three supervised decision forest algorithms, viz., SysFor, ForestPA and Random Forest. * Identifying the lowest number of trees required to achieve maximum detection rate in the decision forests. * Identification of the ideal training and testing split of the acoustic signal data of [PERSON]'s and control subjects has also been carried out for achieving maximum performance. * A detailed investigation has also been carried out to identify the ideal feature segments of the acoustic signals for effective detection of Parkinson. Rest of the article is as follows. Section 2 deals with various literatures of Parkinson's Disease Detection (PDD) using decision forest algorithms. Decision forest algorithms used in this article are detailed in Section 3. Section 4 discusses materials and methods towards the aims and objectives of this article. Section 5 deals with results and discussion followed by conclusion at Section 6 of this article.