Machine Learning Base Methods for Breast Cancer Diagnose

Deng Yang, Yang Yujun, Qiu Laixiang, Zhouyi Wang
{"title":"Machine Learning Base Methods for Breast Cancer Diagnose","authors":"Deng Yang, Yang Yujun, Qiu Laixiang, Zhouyi Wang","doi":"10.1109/ICCWAMTIP56608.2022.10016494","DOIUrl":null,"url":null,"abstract":"Cancer is a serious threat to people's health, and its heterogeneous nature and its ability to divide and proliferate make it difficult to cure. For women around the world, breast cancer has been affecting their health and even the risk of life. Therefore, earlier and more accurate diagnosis can save patient's lives. As research into machine learning has become more advanced, different algorithms have been applied to various datasets, including medical data. In this paper, mainly introduce three algorithms that are commonly used and superior in cancer diagnosis, K-Nearest Neighbor algorithm, Naive Bayesian algorithm based on Bayes' theorem and Support Vector Machine. An experimental case is used to illustrate the F1 score, accuracy and recall rate of these two algorithms on the same data set.","PeriodicalId":159508,"journal":{"name":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCWAMTIP56608.2022.10016494","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Cancer is a serious threat to people's health, and its heterogeneous nature and its ability to divide and proliferate make it difficult to cure. For women around the world, breast cancer has been affecting their health and even the risk of life. Therefore, earlier and more accurate diagnosis can save patient's lives. As research into machine learning has become more advanced, different algorithms have been applied to various datasets, including medical data. In this paper, mainly introduce three algorithms that are commonly used and superior in cancer diagnosis, K-Nearest Neighbor algorithm, Naive Bayesian algorithm based on Bayes' theorem and Support Vector Machine. An experimental case is used to illustrate the F1 score, accuracy and recall rate of these two algorithms on the same data set.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于机器学习的乳腺癌诊断方法
癌症是对人类健康的严重威胁,其异质性及其分裂和增殖能力使其难以治愈。对于世界各地的女性来说,乳腺癌一直在影响她们的健康,甚至危及生命。因此,更早、更准确的诊断可以挽救患者的生命。随着对机器学习的研究越来越先进,不同的算法已经应用于各种数据集,包括医疗数据。本文主要介绍了三种在癌症诊断中较为常用和优越的算法:k -最近邻算法、基于贝叶斯定理的朴素贝叶斯算法和支持向量机。通过一个实验案例,说明了这两种算法在同一数据集上的F1分数、准确率和召回率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Subcortico-Cortical Interactions Of Edge Functional Connectivity In Parkinson’s Disease Feature Modeling and Dimensionality Reduction to Improve ML-Based DDOS Detection Systems in SDN Environment Research on the "Deep Integration" of Information Technology and Precise Civic Education in Universities Knowledge Extraction and Discrimination Based Calibration on Medical Imaging Classification AW-PCNN: Adaptive Weighting Pyramidal Convolutional Neural Network for Fine-Grained Few-Shot Learning
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1