Juhola Martti
Department of Computer Sciences, 33014 University of Tampere, Finland.
Stud Health Technol Inform. 2008;136:211-6.
A dataset including cases of six otoneurological diseases was analysed using machine learning methods to investigate the classification problem of these diseases and to compare the effectiveness of different methods for this data. Linear discriminant analysis was the best method and next multilayer perceptron neural networks provided that the data was input into a network in the form of principal components. Nearest neighbour searching, k-means clustering and Kohonen neural networks achieved almost as good results as the former, but decision trees slightly worse. Thus, these methods fared well, but Naïve Bayes rule could not be used since some data matrices were singular. Otoneurological cases subject to the six diseases given can be reliably distinguished.
使用机器学习方法分析了一个包含六种耳神经科疾病病例的数据集，以研究这些疾病的分类问题，并比较不同方法对该数据的有效性。线性判别分析是最佳方法，其次是多层感知器神经网络，前提是数据以主成分的形式输入到网络中。最近邻搜索、k均值聚类和科霍宁神经网络取得了几乎与前者一样好的结果，但决策树稍差。因此，这些方法表现良好，但由于一些数据矩阵是奇异的，朴素贝叶斯规则无法使用。给定的六种疾病的耳神经科病例可以可靠地区分。
