医疗保健

IF 1.3 Q4 MEDICAL INFORMATICS
R. Selvanambi, Jaisankar N.
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引用次数: 4

摘要

相当长一段时间以来,对癌症治疗进行质量分析一直是电子保健服务的一个目标。目的是通过使用不同的输入参数来预测乳腺癌的阶段。与其他肿瘤相比,乳腺癌是妇女死亡的主要原因之一。乳腺癌信息的分类有助于预测疾病或跟踪肿瘤的遗传。在分类方面,采用了人工神经网络(ANN)结构。在该结构中,使用了9种训练算法,提出的是Levenberg-Marquardt算法。为了优化隐藏层和神经元,使用了三种优化技术。最后,对最佳审批执行情况进行了预测,并对三种优化算法的不同执行情况进行了评价。下图显示了社交蜘蛛优化(social spider optimization, SSO)算法在准确率为95%、灵敏度为98%和特异性为89%时的关联执行图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Healthcare
Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.
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来源期刊
CiteScore
5.20
自引率
0.00%
发文量
18
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