使用机器学习预测皮肤疾病

S. Arjaria, V. Raj, Sunil Kumar, P. Shrivastava, Monu Kumar, Jincy S. Cherian
{"title":"使用机器学习预测皮肤疾病","authors":"S. Arjaria, V. Raj, Sunil Kumar, P. Shrivastava, Monu Kumar, Jincy S. Cherian","doi":"10.4018/978-1-7998-7888-9.ch008","DOIUrl":null,"url":null,"abstract":"Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.","PeriodicalId":371354,"journal":{"name":"Ethical Implications of Reshaping Healthcare With Emerging Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Skin Diseases Using Machine Learning\",\"authors\":\"S. Arjaria, V. Raj, Sunil Kumar, P. Shrivastava, Monu Kumar, Jincy S. Cherian\",\"doi\":\"10.4018/978-1-7998-7888-9.ch008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.\",\"PeriodicalId\":371354,\"journal\":{\"name\":\"Ethical Implications of Reshaping Healthcare With Emerging Technologies\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ethical Implications of Reshaping Healthcare With Emerging Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-7998-7888-9.ch008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ethical Implications of Reshaping Healthcare With Emerging Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-7998-7888-9.ch008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在过去的几十年里,皮肤病的发病率一直在上升。它在世界各地导致了致命和非致命的残疾,特别是在医疗资源不足的地区。皮肤病的早期诊断大大增加了治愈的机会。因此,本工作比较了六种机器学习算法,即KNN、随机森林、神经网络、naïve贝叶斯、逻辑回归和SVM,用于皮肤病的预测。利用信息增益、增益比、基尼系数减小、卡方和起伏度对特征进行排序。这项工作包括引言,文献综述和提出的方法部分。本文提出了一种皮肤病分析的新方法,该方法使用六种不同的数据挖掘技术,开发了一种集成方法,将所有六种数据挖掘技术集成为一种方法。在皮肤病学数据集上使用的集成方法与其他分类器算法相比,准确度提高了94%,因此在该领域更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Skin Diseases Using Machine Learning
Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.
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:604180095
Book学术官方微信