Stroke Prediction Using Smote for Data Balancing, XGBoost and KNN Ensemble Algorithms

O. Abiodun, Andrew Ishaku Wreford
{"title":"Stroke Prediction Using Smote for Data Balancing, XGBoost and KNN Ensemble Algorithms","authors":"O. Abiodun, Andrew Ishaku Wreford","doi":"10.56557/japsi/2023/v15i18349","DOIUrl":null,"url":null,"abstract":"Stroke is a pathological condition characterized by the rupture of blood vessels within the cerebral region, resulting in detrimental effects on the brain. The occurrence of stroke symptoms may arise when there is a disruption in the delivery of blood and essential nutrients to the brain. As per the World Health Organization (WHO), stroke is identified as the primary contributor to mortality and impairment on a worldwide scale. The early identification of stroke symptoms is of utmost importance as it provides vital information for predicting the likelihood of a stroke occurring and encourages the adoption of a healthy lifestyle. This study utilized two ensemble machine learning (ML) algorithms, namely KNN and XGBoost, which were combined in a stacked approach to create and evaluate the models. The primary goal was to establish a robust framework for predicting long-term stroke risk. Hence, the major contributions of this study are the introduction of data balancing techniques using smote algorithm and more importantly the stacking of the KNN and XGBoost algorithm, which exhibits high performance as validated by various metrics, including precision, recall, f-measure, and accuracy. Experimental results demonstrate that the stacked algorithm surpasses other applied ensemble methods, achieving an impressive accuracy of 97%, with a recall of 95% and 98%, precision of 98% and 95%, and an f1 score of 97%.","PeriodicalId":322062,"journal":{"name":"Journal of Applied Physical Science International","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Physical Science International","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.56557/japsi/2023/v15i18349","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

Stroke is a pathological condition characterized by the rupture of blood vessels within the cerebral region, resulting in detrimental effects on the brain. The occurrence of stroke symptoms may arise when there is a disruption in the delivery of blood and essential nutrients to the brain. As per the World Health Organization (WHO), stroke is identified as the primary contributor to mortality and impairment on a worldwide scale. The early identification of stroke symptoms is of utmost importance as it provides vital information for predicting the likelihood of a stroke occurring and encourages the adoption of a healthy lifestyle. This study utilized two ensemble machine learning (ML) algorithms, namely KNN and XGBoost, which were combined in a stacked approach to create and evaluate the models. The primary goal was to establish a robust framework for predicting long-term stroke risk. Hence, the major contributions of this study are the introduction of data balancing techniques using smote algorithm and more importantly the stacking of the KNN and XGBoost algorithm, which exhibits high performance as validated by various metrics, including precision, recall, f-measure, and accuracy. Experimental results demonstrate that the stacked algorithm surpasses other applied ensemble methods, achieving an impressive accuracy of 97%, with a recall of 95% and 98%, precision of 98% and 95%, and an f1 score of 97%.
使用Smote进行数据平衡、XGBoost和KNN集成算法的行程预测
中风是一种以大脑区域血管破裂为特征的病理状态,对大脑产生有害影响。当向大脑输送血液和必需营养物质时,就可能出现中风症状。根据世界卫生组织(世卫组织),中风被确定为世界范围内死亡和损害的主要原因。中风症状的早期识别至关重要,因为它为预测中风发生的可能性提供了至关重要的信息,并鼓励采取健康的生活方式。本研究使用了两种集成机器学习(ML)算法,即KNN和XGBoost,它们以堆叠的方式组合在一起来创建和评估模型。主要目标是建立一个预测长期中风风险的可靠框架。因此,本研究的主要贡献是引入了使用smote算法的数据平衡技术,更重要的是KNN和XGBoost算法的叠加,该算法通过各种指标(包括精度、召回率、f-measure和准确性)验证了高性能。实验结果表明,该算法优于其他应用的集成方法,准确率达到97%,召回率为95%和98%,精密度为98%和95%,f1分数为97%。
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 求助全文
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