Machine learning classifiers are used to distinguish healthy individuals from patients with Parkinson?s disease through the use of a dataset of voice measurements based on patient speech recordings. Feature selection based on information theory is used in many data mining and machine learning applications. Mutual information is used on the Parkinson disease dataset to select a subset of relevant features that contribute the most in the decision making process. In conjunction with Mutual Information, the area under curve (AUC) is applied for feature selection, and features are eliminated by majority voting. In this paper, five classifiers are used to classify Parkinson?s disease: Multilayer Feedforward Artificial Neural Network, k-Nearest Neighbor (kNN), Support Vector Machines, Na?ve Bayes, and k-Means. The dataset is preprocessed prior to the classification, and the classifiers are trained using the k-fold cross validation evaluation model. The performance of the classifiers is evaluated based on the accuracy and the area under curve before and after the feature selection. The results are promising, particularly for the kNN classifier; k-Means presents the worst performance.
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机译：机器学习分类器用于通过使用基于患者语音记录的语音测量数据集来区分帕金森患者患者的健康个体。基于信息理论的特征选择用于许多数据挖掘和机器学习应用。帕金森病DataSet使用互信息，以选择决策过程中最大的相关功能的子集。结合互信息，曲线（AUC）下的区域应用于特征选择，并且通过多数投票消除了特征。在本文中，五分类器用于分类帕金森氏病：多层前馈人工神经网络，K最近邻（knn），支持向量机，Na ve Bayes和K-means。数据集在分类之前预处理，并且使用k折交叉验证评估模型接受分类器。根据特征选择之前和之后的准确性和区域评估分类器的性能。结果是有前途的，特别是KNN分类器; K-means提出了最糟糕的表现。
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