A Comparative Study of Parkinson Disease Diagnosis in Machine Learning
Mohammed Younis Thanoun, Mohammad Tariq Yaseen
Abstract
Parkinson's disease (PD) is a cumulative disorder in the nervous system. PD patients may experience difficulty in movement and speaking due to damages in certain parts in the brain. In this study, we propose using two types of Ensemble learning methods Stacking Classifier and voting classifier, which are potential methods of PD detection using machine learning. Then, we compared between the results of both of them. Stacking Classifier method outperformed voting classifier and the obtained accuracy was 92.2% and 83.57%, respectively. This comparative study would help come out with higher detection accuracy for medical applications such as this chronic disease.
Topics & Concepts
Classifier (UML)Artificial intelligenceComputer scienceMachine learningParkinson's diseaseEnsemble learningVotingStackingPattern recognition (psychology)DiseaseMedicinePathologyPolitical scienceNuclear magnetic resonanceLawPhysicsPoliticsVoice and Speech DisordersParkinson's Disease Mechanisms and TreatmentsVehicle License Plate Recognition