Advances in Detecting Parkinson’s Disease

  • Pei-Fang Guo
  • Prabir Bhattacharya
  • Nawwaf Kharma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6165)


Diagnosing disordered subjects is of considerable importance in medical biometrics. In this study, aimed to provide medical decision boundaries for detecting Parkinson’s disease (PD), we combine genetic programming and the expectation maximization algorithm (GP-EM) to create learning feature functions on the basis of ordinary feature data (features of voice). Via EM, the transformed data are modeled as a Gaussians mixture, so that the learning processes with GP are evolved to fit the data into the modular structure, thus enabling the efficient observation of class boundaries to separate healthy subjects from those with PD. The experimental results show that the proposed biometric detector is comparable to other medical decision algorithms existing in the literature and demonstrates the effectiveness and computational efficiency of the mechanism.


Feature creation medical biometrics genetic programming the expectation maximization algorithm Parkinson’s disease medical decision system 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pei-Fang Guo
    • 1
  • Prabir Bhattacharya
    • 2
  • Nawwaf Kharma
    • 1
  1. 1.Electrical & Computer EngineeringConcordia UniversityMontrealCanada
  2. 2.Fellow, IEEE, Computer Science DepartmentUniversity of CincinnatiCincinnatiUSA

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