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基于列文伯格-马夸尔特训练算法的多层神经网络在肝炎疾病诊断中的应用研究。

A study on hepatitis disease diagnosis using multilayer neural network with levenberg marquardt training algorithm.

机构信息

Department of Electrical and Electronics Engineering, Bozok University, 66200, Yozgat, Turkey.

出版信息

J Med Syst. 2011 Jun;35(3):433-6. doi: 10.1007/s10916-009-9378-2. Epub 2009 Oct 16.

Abstract

In this study, a hepatitis disease diagnosis study was realized using neural network structure. For this purpose, a multilayer neural network structure was used. Levenberg-Marquardt algorithm was used as training algorithm for the weights update of the neural network. The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database. We obtained a classification accuracy of 91.87% via tenfold cross validation.

摘要

在这项研究中,使用神经网络结构实现了肝炎疾病的诊断研究。为此,使用了多层神经网络结构。Levenberg-Marquardt 算法被用作神经网络权重更新的训练算法。研究结果与之前的研究结果进行了比较,这些研究都集中在使用相同 UCI 机器学习数据库的肝炎疾病诊断上。通过十折交叉验证,我们获得了 91.87%的分类准确率。

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