文章基本信息

标题：Severity Prediction over Parkinson's Disease Prediction by Using the Deep Brooke Inception Net Classifier
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作者：R. Sarankumar ; D. Vinod ; K. Anitha 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2022
卷号：2022
DOI：10.1155/2022/7223197
语种：English
出版社：Hindawi Publishing Corporation
摘要：Parkinson's disease (PD) is a neurodegenerative illness that progresses and is long-lasting. It becomes more difficult to talk, write, walk, and do other basic functions when the brain's dopamine-generating neurons are injured or killed. There is a gradual rise in the intensity of these symptoms over time. Using Parkinson's Telemonitoring Voice Data Set from UCI and deep neural networks, we provide a strategy for predicting the severity of Parkinson's disease in this research. An unprocessed speech recording contains a slew of unintelligible data that makes correct diagnosis difficult. Therefore, the raw signal data must be preprocessed using the signal error drop standardization while the features can be grouped by using the wavelet cleft fuzzy algorithm. Then the abnormal features can be selected by using the firming bacteria foraging algorithm for feature size decomposition process. Then classification was made using the deep brooke inception net classifier. The performances of the classifier are compared where the simulation results show that the proposed strategy accuracy in detecting severity of the Parkinson's disease is better than other conventional methods. The proposed DBIN model achieved better accuracy compared to other existing techniques. It is also found that the classification based on extracted voice abnormality data achieves better accuracy (99.8%) over PD prediction; hence it can be concluded as a better metric for severity prediction.