Parkinson's disease (PD) diagnostics includes numerous analyses related to the neurological, physical, and psychical status of the patient. Medical teams analyze multiple symptoms and patient history considering verified genetic influences. The proposed method investigates the voice symptoms of this disease. The voice files are processed, and the feature extraction is conducted. Several machine learning techniques are used to recognize Parkinson's and healthy patients. This study focuses on examining PD diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. The MAFT algorithm is designed to select a set of features to improve the overall performance of prediction models and prevent over-fitting possibly due to extreme reduction to the features. Moreover, this algorithm aims to reduce the complexity of the prediction, accelerate the training phase, and build a robust training model. Ten different machine learning methods are then integrated with the MAFT algorithm to form a powerful voice-based PD diagnosis model. Recorded test results of the PD prediction model using the actual and filtered features yielded 86.38% and 86.67% accuracies on average, respectively. With the aid of the MAFT feature selection, the test results are improved by 3.2% considering the hybrid model (HM) and 3.1% considering the Naïve Bayesian and random forest. Subsequently, an HM, which comprises a binary convolutional neural network and three feature selection algorithms (namely, genetic algorithm, Adam optimizer, and mini-batch gradient descent), is proposed to improve the classification accuracy of the PD. The results reveal that PD achieves an overall accuracy of 93.7%. The HM is integrated with the MAFT, and the combination realizes an overall accuracy of 96.9%. These results demonstrate that the combination of the MAFT algorithm and the HM model significantly enhances the PD diagnosis outcomes.

中文翻译：

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用于帕金森病诊断的多智能体特征选择和混合分类模型

帕金森病 (PD) 诊断包括与患者的神经、身体和心理状态相关的大量分析。考虑到经过验证的遗传影响，医疗团队会分析多种症状和患者病史。所提出的方法调查了这种疾病的声音症状。处理语音文件，进行特征提取。几种机器学习技术用于识别帕金森氏症和健康患者。本研究侧重于通过语音数据特征检查 PD 诊断。提出了一种新的多智能体特征过滤器（MAFT）算法来从语音数据集中选择最佳特征。MAFT 算法旨在选择一组特征来提高预测模型的整体性能，并防止由于特征的极端减少而可能导致的过度拟合。此外，该算法旨在降低预测的复杂性，加速训练阶段，并建立一个健壮的训练模型。然后将十种不同的机器学习方法与 MAFT 算法相结合，形成强大的基于语音的 PD 诊断模型。使用实际和过滤特征的 PD 预测模型的记录测试结果平均分别产生了 86.38% 和 86.67% 的准确度。在 MAFT 特征选择的帮助下，考虑到混合模型 (HM)，测试结果提高了 3.2%，考虑到朴素贝叶斯和随机森林，测试结果提高了 3.1%。随后，提出了一种由二元卷积神经网络和三种特征选择算法（即遗传算法、Adam优化器和小批量梯度下降）组成的HM，以提高PD的分类精度。结果表明，PD 的总体准确率达到了 93.7%。HM与MAFT集成，组合实现了96.9%的整体精度。这些结果表明 MAFT 算法和 HM 模型的结合显着提高了 PD 诊断结果。