# Machine Learning Methods with Decision Forests for Parkinson's Detection
###### Abstract  
Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson's Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson's detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson's Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson's and control subjects donated by the Department of Neurology in Cernabasa, Istanbul and Departamento de Matematicas, Universidad de Extremadura, Caceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson's disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%. Parkinson detection; ForestPA; SysFor; decision forest comparison; training-testing split; decision tree ensemble; machine learning +
Footnote †: journal: Medical Engineering  
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Footnote 1: Department of Computer Applications, Sikikim Manipal Institute of Technology, Sikikim Manipal University, Sikikim 737136, India; [EMAIL_ADDRESS]  
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Footnote 2: Department of Medicine, Sikikim Manipal Institute of Medical Sciences, Sikikim Manipal University, Sikikim 737102, India; [EMAIL_ADDRESS]  
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Footnote 3: Department of Electrical and Electronics Engineering, Sikikim Manipal Institute of Technology, Sikikim Manipal University, Sikikim 737136, India; [EMAIL_ADDRESS]  
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Footnote 4: Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
researchers to detect the presence of [PERSON]'s disease. Out of various options to detect [PERSON]'s, the acoustic signal of voice plays a prominent role in the early detection of the disease. It is because the vocal impairment is found from the beginning of the disease and even years before a clinical diagnosis can be made [16; 17; 18; 19]. There exist many state-of-the-art machine learning approaches to detect the presence of PD at the initial stage [20; 21; 22]. Most of the modern machine learning techniques employ decision trees and few handfuls of approaches rely on decision forests. Though the decision trees proved to be efficient classifying acoustic voice signal data, at the same time decision forests are preferable over traditional decision trees as decision forests are more precise, accurate and able to handle overfitting and underfitting situations smartly through decision tree ensemble. Random Forest, as a random tree ensemble frequently used for [PERSON]'s detection [23; 24], relies on bagging approach of ensemble, which makes the decision forest versatile [25]. However, Random Forest [26] is not the only decision forest that exists in the literature. There are two recently devised approaches in data science, i.e., Systematically Developed Forests (SysFor) [27] and Forest by Penalizing Attributes (ForestPA) [28]. Many literatures suggest that the SysFor, and ForestPA are equally versatile as Random Forest.