期刊
JOURNAL OF HEALTHCARE ENGINEERING
卷 6, 期 3, 页码 281-302出版社
HINDAWI LTD
DOI: 10.1260/2040-2295.6.3.281
关键词
computer aided diagnosis; Parkinson's disease; complex-valued neural network; classification; mRMR feature selection method
Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.
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