Prediction of Alzheimer's in People with Coronavirus Using Machine Learning.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2023-10-01 DOI:10.18502/ijph.v52i10.13856

Shahriar Mohammadi, Soraya Zarei, Hossain Jabbari

{"title":"Prediction of Alzheimer's in People with Coronavirus Using Machine Learning.","authors":"Shahriar Mohammadi, Soraya Zarei, Hossain Jabbari","doi":"10.18502/ijph.v52i10.13856","DOIUrl":null,"url":null,"abstract":"Background: One of the negative effects of the COVID-19 illness, which has affected people all across the world, is Alzheimer's disease. Oblivion after COVID-19 has created a variety of issues for many people. Predicting this issue in COVID-19 patients can considerably lessen the severity of the problem.Methods: Alzheimer's disease was predicted in Iranian persons with COVID-19 in using three algorithms: Nave Bayes, Random Forest, and KNN. Data collected by private questioner from hospitals of Tehran Province, Iran, during Oct 2020 to Sep 2021. For ML models, performance is quantified using measures such as Precision, Recall, Accuracy, and F1-score.Results: The Nave Bayes, Random Forest algorithm has a prediction accuracy of higher than 80%. The predicted accuracy of the random forest algorithm was higher than the other two algorithms.Conclusion: The Random Forest algorithm outperformed the other two algorithms in predicting Alzheimer's disease in persons using COVID-19. The findings of this study could help persons with COVID-19 avoid Alzheimer's problems.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10612562/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.18502/ijph.v52i10.13856","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Background: One of the negative effects of the COVID-19 illness, which has affected people all across the world, is Alzheimer's disease. Oblivion after COVID-19 has created a variety of issues for many people. Predicting this issue in COVID-19 patients can considerably lessen the severity of the problem.

Methods: Alzheimer's disease was predicted in Iranian persons with COVID-19 in using three algorithms: Nave Bayes, Random Forest, and KNN. Data collected by private questioner from hospitals of Tehran Province, Iran, during Oct 2020 to Sep 2021. For ML models, performance is quantified using measures such as Precision, Recall, Accuracy, and F1-score.

Results: The Nave Bayes, Random Forest algorithm has a prediction accuracy of higher than 80%. The predicted accuracy of the random forest algorithm was higher than the other two algorithms.

Conclusion: The Random Forest algorithm outperformed the other two algorithms in predicting Alzheimer's disease in persons using COVID-19. The findings of this study could help persons with COVID-19 avoid Alzheimer's problems.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

使用机器学习预测冠状病毒患者的阿尔茨海默氏症。

背景：新冠肺炎疾病的负面影响之一是阿尔茨海默病，该疾病已影响到世界各地的人们。新冠肺炎后的遗忘给许多人带来了各种各样的问题。在新冠肺炎患者中预测这一问题可以大大减轻问题的严重性。方法：使用Nave Bayes、Random Forest和KNN三种算法预测伊朗新冠肺炎患者的阿尔茨海默病。私人提问者在2020年10月至2021年9月期间从伊朗德黑兰省医院收集的数据。对于ML模型，使用Precision、Recall、Accuracy和F1-score等指标来量化性能。结果：Nave Bayes，Random Forest算法的预测准确率高于80%。随机森林算法的预测精度高于其他两种算法。结论：随机森林算法在预测新冠肺炎患者阿尔茨海默病方面优于其他两种算法。这项研究的发现可以帮助新冠肺炎患者避免阿尔茨海默病问题。

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

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.