使用SVM方法及早发现中风(病例研究:h.e.艾萨克·乌马鲁古·马鲁古中医和紧急生命源医院)

B. P. Tomasouw, F. Y. Rumlawang
{"title":"使用SVM方法及早发现中风(病例研究:h.e.艾萨克·乌马鲁古·马鲁古中医和紧急生命源医院)","authors":"B. P. Tomasouw, F. Y. Rumlawang","doi":"10.30598/tensorvol4iss1pp37-44","DOIUrl":null,"url":null,"abstract":"Stroke is a significant health problem in today's modern society. Early detection of stroke usually takes a long time. To prevent the risk of a significant disabling stroke, it is good to pay attention and recognize the symptoms of a stroke early on. In this study, the Support Vector Machine (SVM) method was used to detect stroke based on risk factors for stroke consisting of blood pressure, age, LDL, and blood sugar. Based on the results obtained, the nonlinear SVM method has a better level of accuracy than the linear SVM. This is because of the two data-sharing schemes, the linear SVM only has an accuracy rate of 81.25%, while the nonlinear SVM has an accuracy rate of 84.38%. Especially for the nonlinear SVM, the RBF kernel has a better level of accuracy than the polynomial kernel. This can be seen from the results of testing the two data sharing schemes, the RBF kernel has the best results, namely the highest accuracy rate of 84.38% and 84% respectively","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Penerapan Metode SVM Untuk Deteksi Dini Penyakit Stroke (Studi Kasus : RSUD Dr. H. Ishak Umarella Maluku Tengah dan RS Sumber Hidup-GPM)\",\"authors\":\"B. P. Tomasouw, F. Y. Rumlawang\",\"doi\":\"10.30598/tensorvol4iss1pp37-44\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Stroke is a significant health problem in today's modern society. Early detection of stroke usually takes a long time. To prevent the risk of a significant disabling stroke, it is good to pay attention and recognize the symptoms of a stroke early on. In this study, the Support Vector Machine (SVM) method was used to detect stroke based on risk factors for stroke consisting of blood pressure, age, LDL, and blood sugar. Based on the results obtained, the nonlinear SVM method has a better level of accuracy than the linear SVM. This is because of the two data-sharing schemes, the linear SVM only has an accuracy rate of 81.25%, while the nonlinear SVM has an accuracy rate of 84.38%. Especially for the nonlinear SVM, the RBF kernel has a better level of accuracy than the polynomial kernel. This can be seen from the results of testing the two data sharing schemes, the RBF kernel has the best results, namely the highest accuracy rate of 84.38% and 84% respectively\",\"PeriodicalId\":294430,\"journal\":{\"name\":\"Tensor: Pure and Applied Mathematics Journal\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tensor: Pure and Applied Mathematics Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30598/tensorvol4iss1pp37-44\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tensor: Pure and Applied Mathematics Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30598/tensorvol4iss1pp37-44","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

中风是当今现代社会一个重要的健康问题。早期发现中风通常需要很长时间。为了防止严重致残中风的风险,最好在早期注意并识别中风的症状。本研究采用支持向量机(SVM)方法,基于血压、年龄、LDL、血糖等脑卒中危险因素对脑卒中进行检测。结果表明,非线性支持向量机方法比线性支持向量机方法具有更高的精度。这是因为两种数据共享方案,线性支持向量机的准确率仅为81.25%,而非线性支持向量机的准确率为84.38%。特别是对于非线性支持向量机,RBF核比多项式核具有更高的精度。从两种数据共享方案的测试结果可以看出,RBF内核的效果最好,准确率最高,分别为84.38%和84%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Penerapan Metode SVM Untuk Deteksi Dini Penyakit Stroke (Studi Kasus : RSUD Dr. H. Ishak Umarella Maluku Tengah dan RS Sumber Hidup-GPM)
Stroke is a significant health problem in today's modern society. Early detection of stroke usually takes a long time. To prevent the risk of a significant disabling stroke, it is good to pay attention and recognize the symptoms of a stroke early on. In this study, the Support Vector Machine (SVM) method was used to detect stroke based on risk factors for stroke consisting of blood pressure, age, LDL, and blood sugar. Based on the results obtained, the nonlinear SVM method has a better level of accuracy than the linear SVM. This is because of the two data-sharing schemes, the linear SVM only has an accuracy rate of 81.25%, while the nonlinear SVM has an accuracy rate of 84.38%. Especially for the nonlinear SVM, the RBF kernel has a better level of accuracy than the polynomial kernel. This can be seen from the results of testing the two data sharing schemes, the RBF kernel has the best results, namely the highest accuracy rate of 84.38% and 84% respectively
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