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 Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, 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%.

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

“…Gender-specific control and sick subjects are outlined in Table 2 . The detailed characteristics of these features segments and corresponding features can be found at [ 24 , 27 ].…”

“…The Naïve Bayes classifier on AGWOA and SAE features reveals a detection accuracy of 72%. In the recent past, decision trees are gaining popularity in biomedical data classification [ 27 ]. Classification and Regression Tree (CART) have been used to detect the presence of Parkinson's [ 28 ], where the CART detector detects Parkinson's with 75.19% through 8 optimum features of vowel /a/.…”

Machine Learning Model to Analyze Telemonitoring Dyphosia Factors of Parkinson’s Disease

“…Also, SMOTE method was used to balance PD dataset. Paramanik et al [25] used two recent decision forest algorithms such as SysFor, ForestPA including RF for developing PD detection models with the optimization of DT.…”

Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem

“…These technologies include deep learning (DL) [8], genetic algorithm (GA) [9], and XGBoost method [10]. A study used an ensemble learning classifier, which can provide a high prediction accuracy [11]. Although considerable efforts have been taken to make models highly accurate for PD prediction, the task remains challenging due to numerous reasons.…”

“…Pramanik et al [11] used enhanced decision forest algorithms using systematically developed forest (SysFor), with penalising attributes (ForestPA), and public random forest algorithms and compared the results for two groups of recent acoustic data on PD. Decision forest with penalising attributes is the optimum solution for detecting PD with an accuracy of 94.12-95%.…”