Ameer K. Al-Mashanji, L. H. Alhasnawy, Aseel Hamoud Hamza
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引用次数: 0
摘要
帕金森病(PD)是一种慢性进展的神经退行性综合征。由于产生多巴胺的大脑神经元受损或死亡,患者在进行简单的日常活动时,如走路、写作或说话,往往会面临困难。这项工作的主要贡献是提出了一种改进PD预测的混合方法。这种方法是通过测试用于选择属性的分类算法和方法的许多不同组合而获得的。总共采用了三种属性选择方法(相关性、信息增益和方差阈值)和三种分类器(决策树(DT)、朴素贝叶斯(NB)和支持向量机(SVM))。采用加州大学欧文分校(University of California-Irvine, UCI)机器学习(ML)知识库提供的语音数据集,分析不同组合的性能。信息增益与DT分类器相结合的分类准确率达到97.43%,优于其他组合方法。最后,将性能分析与先前研究的结果进行了额外的比较,发现所提出的方法证明优于在该领域进行的其他研究的结果。
Robust attribute selection to improve the Parkinson's disease classification: a hybrid approach
Parkinson’s disease (PD) is known to be a neurodegenerative syndrome that progresses chronically. As a result of the damage or death of brain neurons that generate dopamine patients tend to face difficulty when performing simple everyday tasks like walking, writing, or speaking. The main contribution of this work presents a hybrid method for improving predicting PD. This methodology has been obtained by means of testing a number of different combinations of classification algorithms and approaches for selecting attributes. A total of three attributes selection methods (correlation, information gain, and variance threshold) and three classifiers (decision trees (DT), naive bayes (NB), and support vector machine (SVM)) have been adopted. The speech data set provided by University of California-Irvine (UCI) machine learning (ML) repository is adopted to analyze the performance of different combinations. The combination of information gain and DT classifier achieved the best performance rather than other combination methods, reaching a classification accuracy of (97.43%). Finally, an additional comparison of the performance analysis with the results of previous studies was made and it was found that the proposed methodology proved to outperform the results of other studies conducted in this field.
期刊介绍:
Bulletin of Electrical Engineering and Informatics publishes original papers in the field of electrical, computer and informatics engineering which covers, but not limited to, the following scope: Computer Science, Computer Engineering and Informatics[...] Electronics[...] Electrical and Power Engineering[...] Telecommunication and Information Technology[...]Instrumentation and Control Engineering[...]