Parkinson’s disease (PD) is a prevalent, and progressive neurological disorder. Due to the motor and non-motor symptoms of the disease, it lowers life quality of the patients. Tremor, rigidity, depression, anxiety etc. are among the symptoms. Clinical diagnosis of PD is usually based on appearance of motor features. Additionally, different empirical tests were proposed by scholars for early detection of the disease. It is known that people with PD have speech impairments. Therefore, voice tests are used for early detection of the disease. In this study, an automated machine learning system was proposed for high accuracy classification of the speech signals of PD patients. The system includes feature reduction methods and classification algorithms. Feature reductions and classifications were performed for all participants, males, and females separately. Contributions of feature sets to classification accuracy were discussed. Experimental results were evaluated with different performance metrics. The proposed system obtained state of the art results in all categories. We acquired better performances for gender based classifications.

中文翻译：

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用于神经退行性疾病检测的特征驱动智能系统：语音数据集在帕金森病诊断中的应用

帕金森病 (PD) 是一种普遍存在的进行性神经系统疾病。由于疾病的运动和非运动症状，它降低了患者的生活质量。震颤、僵硬、抑郁、焦虑等都是症状。PD的临床诊断通常基于运动特征的出现。此外，学者们提出了不同的经验测试来早期发现这种疾病。众所周知，患有 PD 的人有语言障碍。因此，语音测试用于早期发现疾病。在这项研究中，提出了一种自动化机器学习系统，用于对 PD 患者的语音信号进行高精度分类。该系统包括特征减少方法和分类算法。对所有参与者（男性、和女性分开。讨论了特征集对分类准确性的贡献。使用不同的性能指标评估实验结果。所提出的系统在所有类别中都获得了最先进的结果。我们在基于性别的分类方面获得了更好的表现。