Machine Learning Approaches For Disease Prediction:- A Review

Sudha, Harkesh Sehrawat, Yudhvir Singh, Vivek Jaglan
{"title":"Machine Learning Approaches For Disease Prediction:- A Review","authors":"Sudha, Harkesh Sehrawat, Yudhvir Singh, Vivek Jaglan","doi":"10.1109/AIC55036.2022.9848838","DOIUrl":null,"url":null,"abstract":"Over recent years, disease prediction catches the attention of researcher’s awareness to cover a large range in medical as well as computer science field. Therefore, several models have been constructed for many different-different diseases diagnose and their forecasting. These models utilise an assortment of patient features to assess the likelihood of results over a definite interval of time and have capability to make better decision making. Patients’ health database contain large amount of information regarding particular disease and several laboratory test results. It has become essential to discover hidden patterns from those longitudinal health-related databases, and machine learning algorithms are playing a vital role to achieve this task. These algorithms assure the superior accuracy of observation and identification of disease. This paper highlighting various diseases, whose diagnose and prediction have been done through machine learning algorithms. It conveys concentration in the direction of machine learning algorithms and attributes that are used for the prediction of diseases and decision-making process accordingly.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"298 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIC55036.2022.9848838","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

Over recent years, disease prediction catches the attention of researcher’s awareness to cover a large range in medical as well as computer science field. Therefore, several models have been constructed for many different-different diseases diagnose and their forecasting. These models utilise an assortment of patient features to assess the likelihood of results over a definite interval of time and have capability to make better decision making. Patients’ health database contain large amount of information regarding particular disease and several laboratory test results. It has become essential to discover hidden patterns from those longitudinal health-related databases, and machine learning algorithms are playing a vital role to achieve this task. These algorithms assure the superior accuracy of observation and identification of disease. This paper highlighting various diseases, whose diagnose and prediction have been done through machine learning algorithms. It conveys concentration in the direction of machine learning algorithms and attributes that are used for the prediction of diseases and decision-making process accordingly.
疾病预测的机器学习方法:综述
近年来,疾病预测引起了研究者的关注,在医学和计算机科学领域都有广泛的应用。因此,建立了多种不同疾病的诊断和预测模型。这些模型利用各种各样的患者特征来评估在一定时间间隔内结果的可能性,并有能力做出更好的决策。患者健康数据库包含大量关于特定疾病的信息和几种实验室检测结果。从这些与健康相关的纵向数据库中发现隐藏的模式变得至关重要,机器学习算法在实现这一任务方面发挥着至关重要的作用。这些算法保证了观察和识别疾病的高准确性。本文重点介绍了通过机器学习算法进行诊断和预测的各种疾病。它传达了机器学习算法和属性方向的集中,用于预测疾病和相应的决策过程。
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