| dc.description.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. | en_US |
