Cardiovascular Disease Prediction Using Machine Learning Models

Atharv Nikam, Sanket Bhandari, Aditya Mhaske, Shamla Mantri
{"title":"Cardiovascular Disease Prediction Using Machine Learning Models","authors":"Atharv Nikam, Sanket Bhandari, Aditya Mhaske, Shamla Mantri","doi":"10.1109/PuneCon50868.2020.9362367","DOIUrl":null,"url":null,"abstract":"Cardiovascular diseases are one of the most vital causes offatality. Cardiovascular disease prediction is a critical challenge in the area of clinical data analysis. Machine learning and Neural Networks are more promising in assisting decide and predict from the massive data produced by healthcare. We have noted different features had used in recent developments of the machine learning model. In this paper, we proposed machine learning techniques to predict cardiovascular disease using features. BMI is one of the highlighting features we used for prediction. BMI is important in predicting cardiovascular disease. The main focus of the article isthe effect ofBMI onthe prediction of cardiovascular disease. The model has proposed with different features as well as regression and classification techniques. We conclude that BMI is a significant factor while predicting cardiovascular disease.","PeriodicalId":368862,"journal":{"name":"2020 IEEE Pune Section International Conference (PuneCon)","volume":"356 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Pune Section International Conference (PuneCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PuneCon50868.2020.9362367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

Cardiovascular diseases are one of the most vital causes offatality. Cardiovascular disease prediction is a critical challenge in the area of clinical data analysis. Machine learning and Neural Networks are more promising in assisting decide and predict from the massive data produced by healthcare. We have noted different features had used in recent developments of the machine learning model. In this paper, we proposed machine learning techniques to predict cardiovascular disease using features. BMI is one of the highlighting features we used for prediction. BMI is important in predicting cardiovascular disease. The main focus of the article isthe effect ofBMI onthe prediction of cardiovascular disease. The model has proposed with different features as well as regression and classification techniques. We conclude that BMI is a significant factor while predicting cardiovascular disease.
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
使用机器学习模型预测心血管疾病
心血管疾病是最重要的死亡原因之一。心血管疾病预测是临床数据分析领域的一个重大挑战。机器学习和神经网络更有希望从医疗保健产生的大量数据中帮助决策和预测。我们已经注意到在机器学习模型的最新发展中使用了不同的特征。在本文中,我们提出了使用特征来预测心血管疾病的机器学习技术。BMI是我们用于预测的突出特征之一。BMI在预测心血管疾病方面很重要。本文的主要重点是bmi在心血管疾病预测中的作用。提出了具有不同特征的模型以及回归和分类技术。我们得出结论,BMI是预测心血管疾病的重要因素。
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
Pulmonary CT Images Segmentation using CNN and UNet Models of Deep Learning Feature-Based Landslide Susceptibility and Hazard Zonation Maps using Fuzzy Overlay Analysis Impact of Driving Style on Battery Life of the Electric Vehicle Face and Palmprint Biometric Recognition by using Weighted Score Fusion Technique Nature of CSF based on Beating Time in Fibre Reinforced Cotton Rag
×
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