Holo熵使决策树分类器乳腺癌诊断使用威斯康星(预后)数据集

Shabina Sayed, Shoeb Ahmed, R. Poonia
{"title":"Holo熵使决策树分类器乳腺癌诊断使用威斯康星(预后)数据集","authors":"Shabina Sayed, Shoeb Ahmed, R. Poonia","doi":"10.1109/CSNT.2017.8418532","DOIUrl":null,"url":null,"abstract":"The breast cancer diagnostic and prognostic problems are mainly in the scope of the widely discussed classification problems. These problems have attracted many researchers in computational intelligence, data mining, and statistics fields. The objective of these predictions is to handle cases for which cancer has not recurred (censored data) as well as case for which cancer has recurred at a specific time. The proposed study uses Breast Cancer Wisconsin (Prognostic) Data Set for training and testing purpose. It has implemented holo entropy enable decision tree(HDT). The proposed strategy utilizes the training data to train the classifier. It categorizes each instance of breast cancer growth as recurrent or non recurrent. It ascertains the precision of the classifier to decide the exact classifier accuracy. In the present situation where there is continuous increment in the breast cancer cases and the expanding number of death cases the proposed strategy can be a guide in the determination of breast cancer.","PeriodicalId":382417,"journal":{"name":"2017 7th International Conference on Communication Systems and Network Technologies (CSNT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Holo entropy enabled decision tree classifier for breast cancer diagnosis using wisconsin (prognostic) data set\",\"authors\":\"Shabina Sayed, Shoeb Ahmed, R. Poonia\",\"doi\":\"10.1109/CSNT.2017.8418532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The breast cancer diagnostic and prognostic problems are mainly in the scope of the widely discussed classification problems. These problems have attracted many researchers in computational intelligence, data mining, and statistics fields. The objective of these predictions is to handle cases for which cancer has not recurred (censored data) as well as case for which cancer has recurred at a specific time. The proposed study uses Breast Cancer Wisconsin (Prognostic) Data Set for training and testing purpose. It has implemented holo entropy enable decision tree(HDT). The proposed strategy utilizes the training data to train the classifier. It categorizes each instance of breast cancer growth as recurrent or non recurrent. It ascertains the precision of the classifier to decide the exact classifier accuracy. In the present situation where there is continuous increment in the breast cancer cases and the expanding number of death cases the proposed strategy can be a guide in the determination of breast cancer.\",\"PeriodicalId\":382417,\"journal\":{\"name\":\"2017 7th International Conference on Communication Systems and Network Technologies (CSNT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Communication Systems and Network Technologies (CSNT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSNT.2017.8418532\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Communication Systems and Network Technologies (CSNT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSNT.2017.8418532","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

乳腺癌的诊断和预后问题主要是在广泛讨论的范围内的分类问题。这些问题吸引了许多计算智能、数据挖掘和统计领域的研究人员。这些预测的目的是处理癌症未复发的病例(经过审查的数据)以及癌症在特定时间复发的病例。拟议的研究使用乳腺癌威斯康星(预后)数据集进行培训和测试。它实现了全熵使能决策树(HDT)。该策略利用训练数据来训练分类器。它将每一种乳腺癌的生长情况分为复发性和非复发性。通过确定分类器的精度来确定准确的分类器精度。在目前乳腺癌病例不断增加、死亡人数不断增加的情况下,拟议的战略可以作为确定乳腺癌的指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Holo entropy enabled decision tree classifier for breast cancer diagnosis using wisconsin (prognostic) data set
The breast cancer diagnostic and prognostic problems are mainly in the scope of the widely discussed classification problems. These problems have attracted many researchers in computational intelligence, data mining, and statistics fields. The objective of these predictions is to handle cases for which cancer has not recurred (censored data) as well as case for which cancer has recurred at a specific time. The proposed study uses Breast Cancer Wisconsin (Prognostic) Data Set for training and testing purpose. It has implemented holo entropy enable decision tree(HDT). The proposed strategy utilizes the training data to train the classifier. It categorizes each instance of breast cancer growth as recurrent or non recurrent. It ascertains the precision of the classifier to decide the exact classifier accuracy. In the present situation where there is continuous increment in the breast cancer cases and the expanding number of death cases the proposed strategy can be a guide in the determination of breast cancer.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信