Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach
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DOI：
10.1007/s11517-016-1508-7
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发表时间：
2017-01-01
影响因子：
3.2
通讯作者：
Tavares, Joao Manuel R. S.
中科院分区：
文献类型：
--
作者：
Ahmed, Sk. Saddam;Dey, Nilanjan;Tavares, Joao Manuel R. S.
克罗恩病（CD）的诊断是一个非常严重的健康问题，由于其最终影响胃肠道，导致需要复杂的医疗援助。在这项研究中，反向传播神经网络模糊分类器和神经模糊模型相结合的CD诊断。因子分析用于数据降维。当使用模糊划分和降维对系统性能的影响进行了研究。此外，进一步的比较之间的不同层次的模糊划分，以达到最佳的性能精度水平。所提出的系统的性能评估估计使用的分类精度和其他指标。实验结果表明，8级划分的分类提供了97.67%的分类准确率，96.07%的灵敏度和特异性分别为100%。
Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.