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

• Published in: Journal of healthcare engineering Vol. 6; no. 3; pp. 281 - 302
• Main Authors: Peker, Musa, Şen, Baha, Delen, Dursun
• Format: Journal Article
• Language: English
• Published: England 2015
• Subjects: Algorithms; Diagnosis, Computer-Assisted; Humans; Neural Networks (Computer); Parkinson Disease - diagnosis; Phonation
• ISSN: 2040-2295; 2040-2309; 2040-2309
• DOI: 10.1260/2040-2295.6.3.281

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.