神经网络在肝病分类中的实现

Tristan Joseph C. Limchesing, N. Bugtai, R. Baldovino
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引用次数: 1

摘要

肝脏是一个有价值的内部器官,然而有不同的实体通过这个器官使它容易受到各种疾病的影响。本文介绍了使用智能系统技术来诊断患者可能的肝脏疾病。使用人工神经网络(ANN),输入来自加州大学欧文分校(UCI)存储库的肝脏患者数据集作为训练数据,构建有效的预测模型。利用混淆矩阵对所建立的模型进行了准确性检验。所提出的网络模型能够达到70%左右的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of Neural Network for the Liver Disease Classification
The liver is a valuable internal organ, however having different entities pass through this organ makes it vulnerable to various pathologies. This paper presents using an intelligent system technique to diagnose a patient for possible liver condition. Using an artificial neural network (ANN), the liver patient dataset from the University of California, Irvine (UCI) repository was inputted to be used as training data to make an effective predictive model. The developed model was tested for accuracy by using a confusion matrix. The proposed network model was able to attain an accuracy of around 70%.
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