An Analysis Of Vocal Features For Parkinson's Disease Classification

Abstract

Parkinson's Disease is no longer a strange concept when addressing neurological condition that specifically causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing Parkinson's Disease. This disorder can have severe consequences that affect the patient's daily life. Automatic Parkinson's Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. The basis this study is to propose a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) algorithm for feature selection, along with Light Gradient Boosted Machine to optimize the model performance. The proposed method shows prominent results and has the ability to further develop.