Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm
JOURNAL OF HEALTHCARE ENGINEERING, cilt.6, sa.3, ss.281-302, 2015 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 6 Sayı: 3
- Basım Tarihi: 2015
- Doi Numarası: 10.1260/2040-2295.6.3.281
- Dergi Adı: JOURNAL OF HEALTHCARE ENGINEERING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.281-302
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
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.