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- W4387486517 abstract "There are millions of people throughout the globe who are dealing with the degenerative neurological ailment known as Parkinson's disease (PD). Improving patient outcomes requires prompt diagnosis and treatment. Diagnosis has traditionally relied on clinical observation and medical history; this method is not only inefficient, but it may also lead to a wrong diagnosis. Due to recent developments in machine learning, algorithms have been developed that can accurately predict PD based on patient speech features. In this work, we look at the feasibility of using machine learning models to make PD predictions based on individual patients' vocal characteristics. We used a dataset that included measurements of many sound features, such as fundamental frequency shift, amplitude fluctuation, and nonlinear dynamical complexity. According to our findings, machine learning systems can accurately identify PD from voice features. These algorithms have the potential to aid in the early diagnosis and treatment of Parkinson's disease, which in turn would improve clinical results and the quality of life for those living with the disease. The study emphasizes the need for more research into this field to improve patient outcomes and healthcare, as well as the potential of machine learning in medical diagnostics." @default.
- W4387486517 created "2023-10-11" @default.
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- W4387486517 date "2023-08-25" @default.
- W4387486517 modified "2023-10-16" @default.
- W4387486517 title "Study on Degenerative Parkinson's Disease Using Various Machine Learning Algorithms" @default.
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- W4387486517 doi "https://doi.org/10.1109/asiancon58793.2023.10270216" @default.
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