Journal of Healthcare Engineering

Journal of Healthcare Engineering / 2015 / Article

Research Article | Open Access

Volume 6 |Article ID 493865 | https://doi.org/10.1260/2040-2295.6.3.281

Musa Peker, Baha Sen, Dursun Delen, "Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm", Journal of Healthcare Engineering, vol. 6, Article ID 493865, 22 pages, 2015. https://doi.org/10.1260/2040-2295.6.3.281

Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

Received01 Feb 2015
Accepted01 Jun 2015

Abstract

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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