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Advances in Detecting Parkinson's Disease

机译：检测帕金森病的进展

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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 al gorithm (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.
机译：诊断紊乱的受试者在医学生物学学中具有相当重要的重要性。在这项研究中，旨在提供检测帕金森病（PD）的医学决策界限，我们将遗传编程和期望最大化AL Gorithm（GP-EM）基于普通特征数据（语音的特征）来创建学习功能功能。通过EM，转换的数据被建模为高斯混合，从而演化了与GP的学习过程以将数据拟合到模块化结构中，从而能够高效地观察类边界，以将来自PD的人分开的健康受试者。实验结果表明，所提出的生物识别探测器与文献中存在的其他医学决策算法相当，并展示了机制的有效性和计算效率。

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