Abstract
The progressive reduction of dopaminergic neurons in the human brain, especially at the substantia nigra is one of the principal causes of Parkinson’s Disease (PD). Voice alteration is one of the earliest symptoms found in PD patients. Therefore, the impaired PD subjects’ acoustic voice signal plays a crucial role in detecting the presence of Parkinson's. This manuscript presents four distinct decision tree ensemble methods of PD detection on a trailblazing ForEx++ rule-based framework. The Systematically Developed Forest (SysFor) and a Penalizing Attributes Decision Forest (ForestPA) ensemble approaches has been used for PD detection. The proposed detection schemes efficiently identify positive subjects using primary voice signal features, viz., baseline, vocal fold, and time–frequency. A novel feature selection scheme termed Feature Ranking to Feature Selection (FRFS) has also been proposed to combine filter and wrapper strategies. The proposed FRFS scheme encompasses Gel’s normality test to rank and selects outstanding features from baseline, time–frequency, and vocal fold feature groups. The SysFor and ForestPA decision forests underneath the ForEx++ rule-based framework on both FRFS feature ranking and subset selection represents Parkinson’s detection approaches, which expedite a better overall impact on segregating PD from control subjects. It has been observed that the ForestPA decision forest in the ForEx++ framework on FRFS ranked features proved to be a robust Parkinson’s detection scheme. The proposed models deliver the highest accuracy of 94.12% and a lowest mean absolute error of 0.25, resulting in an Area Under Curve (AUC) value of 0.97.
Highlights
The aged people suffered from a common neurodegenerative disorder often recognized as Parkinson’s Disease
The feature selection outcome of the Feature Ranking to Feature Selection (FRFS) scheme has been explored in detail
The proposed models are explored using a variety of performance measures, i.e., Accuracy (ACC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), False Negative Rate (FNR), False Positive Rate (FPR), Specificity (SPE), Sensitivity (SEN), The proposed FRFS-DTN feature ranking scheme inspired by the directed normality test has been designed for varying densities of data points
Summary
The aged people suffered from a common neurodegenerative disorder often recognized as Parkinson’s Disease. The disease associated with the human nervous system is often observed due to the anomalies of dopaminergic neurons located in the substantia nigra (Tuncer et al 2020). The organ movement becomes very slow, tremor, rigidity, postural instability, walking/gait problems, decreased smell perception, sleep disturbances, and most importantly, variation in speech (Gómez-Vilda et al 2017; Gupta et al 2018; Tuncer and Dogan 2019; Aich et al 2018; Tuncer et al 2020). The diagnosis of PD mostly depends on medical practitioners' motor tests when two out of three symptoms, namely akinesia (challenging to start a movement), rigidity, and rest tremor are observed. As the disease is not reversible, preventive measures at an early stage and clinical intervention are necessary before the disease starts showing adverse symptoms
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Ambient Intelligence and Humanized Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
