慢性病诊断中的预测模型

Anshuman Samantaray, Dr. Sujit Ku Panda
{"title":"慢性病诊断中的预测模型","authors":"Anshuman Samantaray, Dr. Sujit Ku Panda","doi":"10.55041/ijsrem36558","DOIUrl":null,"url":null,"abstract":"This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately,22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among clustering were the most used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future. Keywords: chronic diseases; prediction models; pathologies; accuracy; disease classification; K-Nearest Neighbors (KNN); Convolutional Neural Networks(CNN); disease forecasting; disease management.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"62 12","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive Models in the Chronic Disease Diagnosis\",\"authors\":\"Anshuman Samantaray, Dr. Sujit Ku Panda\",\"doi\":\"10.55041/ijsrem36558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately,22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among clustering were the most used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future. Keywords: chronic diseases; prediction models; pathologies; accuracy; disease classification; K-Nearest Neighbors (KNN); Convolutional Neural Networks(CNN); disease forecasting; disease management.\",\"PeriodicalId\":504501,\"journal\":{\"name\":\"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT\",\"volume\":\"62 12\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55041/ijsrem36558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55041/ijsrem36558","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

本文回顾了机器学习(ML)预测模型在慢性病诊断中的应用。慢性疾病(CD)占全球医疗成本的很大一部分。这些疾病的患者需要终生治疗。如今,预测模型经常被应用于这些疾病的诊断和预测。在本研究中,我们回顾了将 ML 模型应用于 CD 初诊的最先进方法。该分析涵盖了2015年至2019年期间发表的453篇论文,我们的文献检索来自PubMed(Medline)和Cumulative Index to Nursing and Allied Health Literature(CINAHL)图书馆。最终,我们选择了 22 篇研究,以精确的方式介绍所有建模方法,解释 CD 诊断和个别病症的使用模型,以及相关的优势和局限性。我们的研究结果表明,在实时临床实践中没有标准方法来确定最佳方法,因为每种方法都有其优缺点。其中使用最多的是聚类。这些模型非常适用于慢性疾病的分类和诊断,预计在不久的将来会在医疗实践中变得更加重要。关键词: 慢性疾病;预测模型;病理;准确性;疾病分类;K-近邻(KNN);卷积神经网络(CNN);疾病预测;疾病管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Predictive Models in the Chronic Disease Diagnosis
This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately,22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among clustering were the most used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future. Keywords: chronic diseases; prediction models; pathologies; accuracy; disease classification; K-Nearest Neighbors (KNN); Convolutional Neural Networks(CNN); disease forecasting; disease management.
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
期刊最新文献
Development of Pear Fruit RTS Beverage AN OVERVIEW OF MACHINE LEARNING ALGORITHMS FOR WIRELESS SENSOR NETWORKS Impact of Digital Transformation on Indian Manufacturing Industry AI use in Automated Disaster Recovery for IT Applications in Multi Cloud Structural Health Monitoring Using IOT
×
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