使用机器学习预测从认知正常到阿尔茨海默病的进展

IF 0.9 Q3 EDUCATION & EDUCATIONAL RESEARCH
Ann Song
{"title":"使用机器学习预测从认知正常到阿尔茨海默病的进展","authors":"Ann Song","doi":"10.47611/jsrhs.v12i2.4347","DOIUrl":null,"url":null,"abstract":"Alzheimer's Disease (AD) affects approximately 50 million individuals worldwide and is estimated to rise to 152 million by 2050. There is currently no treatment for AD that halts the progression from cognitively normal (CN) and/or mild cognitive impairment (MCI) to AD. The ability to predict disease progression will allow for early treatment. While Machine Learning (ML) has been successful in diagnosing the cognitive state, further improvement is necessary for predicting progression. In this study, Random Forest and Bagging Decision Tree Recursive Feature Elimination (RFE) was utilized to ascertain the cognitive state and forecast progression. Clinical diagnoses, demographics, and post-processed PET and MRI scans used in this study were obtained from the Open Access Series of Imaging Studies (OASIS). The findings suggest that aging and lower levels of education are associated with higher risk. The study found that ML using post-processed MRI and PET scans, particularly RFE ML, is effective in diagnosing cognitive states with 90% accuracy. It can predict progression from CN to MCI or AD with 85% accuracy, which is significantly higher than the average reported in literature. Patients with progression from CN to AD were distinguished by elevated amyloid deposition, hippocampus and amygdala atrophy, left accumbens atrophy, thinning of the left hemisphere temporal, and enlarged inferior lateral ventricles. The study demonstrated that RFE ML is effective in diagnosing and predicting the progression of AD. Future studies will concentrate on identifying the specific regions of amyloid plaque that have the most significant impact on cognitive state and progression. \n ","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Machine Learning to Forecast Progression from Cognitively Normal to Alzheimer's Disease\",\"authors\":\"Ann Song\",\"doi\":\"10.47611/jsrhs.v12i2.4347\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Alzheimer's Disease (AD) affects approximately 50 million individuals worldwide and is estimated to rise to 152 million by 2050. There is currently no treatment for AD that halts the progression from cognitively normal (CN) and/or mild cognitive impairment (MCI) to AD. The ability to predict disease progression will allow for early treatment. While Machine Learning (ML) has been successful in diagnosing the cognitive state, further improvement is necessary for predicting progression. In this study, Random Forest and Bagging Decision Tree Recursive Feature Elimination (RFE) was utilized to ascertain the cognitive state and forecast progression. Clinical diagnoses, demographics, and post-processed PET and MRI scans used in this study were obtained from the Open Access Series of Imaging Studies (OASIS). The findings suggest that aging and lower levels of education are associated with higher risk. The study found that ML using post-processed MRI and PET scans, particularly RFE ML, is effective in diagnosing cognitive states with 90% accuracy. It can predict progression from CN to MCI or AD with 85% accuracy, which is significantly higher than the average reported in literature. Patients with progression from CN to AD were distinguished by elevated amyloid deposition, hippocampus and amygdala atrophy, left accumbens atrophy, thinning of the left hemisphere temporal, and enlarged inferior lateral ventricles. The study demonstrated that RFE ML is effective in diagnosing and predicting the progression of AD. Future studies will concentrate on identifying the specific regions of amyloid plaque that have the most significant impact on cognitive state and progression. \\n \",\"PeriodicalId\":46753,\"journal\":{\"name\":\"Journal of Student Affairs Research and Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2023-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Student Affairs Research and Practice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47611/jsrhs.v12i2.4347\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Student Affairs Research and Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47611/jsrhs.v12i2.4347","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0

摘要

阿尔茨海默病(AD)影响全球约5000万人,预计到2050年将增加到1.52亿人。目前还没有治疗阿尔茨海默病的方法可以阻止从认知正常(CN)和/或轻度认知障碍(MCI)向阿尔茨海默病的进展。预测疾病进展的能力将允许早期治疗。虽然机器学习(ML)在诊断认知状态方面已经取得了成功,但在预测进展方面还需要进一步的改进。在本研究中,随机森林和Bagging决策树递归特征消除(RFE)被用于确定认知状态和预测进程。本研究中使用的临床诊断、人口统计、后处理PET和MRI扫描数据均来自影像研究开放获取系列(OASIS)。研究结果表明,年龄和低教育水平与更高的风险有关。研究发现,使用后处理MRI和PET扫描的ML,特别是RFE ML,在诊断认知状态方面具有90%的准确率。该方法预测CN向MCI或AD进展的准确率为85%,显著高于文献报道的平均水平。从CN进展到AD的患者表现为淀粉样蛋白沉积升高、海马和杏仁核萎缩、左伏隔脑萎缩、左半球颞叶变薄、下侧脑室增大。研究表明,RFE ML在诊断和预测AD进展方面是有效的。未来的研究将集中在确定淀粉样斑块的特定区域,对认知状态和进展有最重要的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Machine Learning to Forecast Progression from Cognitively Normal to Alzheimer's Disease
Alzheimer's Disease (AD) affects approximately 50 million individuals worldwide and is estimated to rise to 152 million by 2050. There is currently no treatment for AD that halts the progression from cognitively normal (CN) and/or mild cognitive impairment (MCI) to AD. The ability to predict disease progression will allow for early treatment. While Machine Learning (ML) has been successful in diagnosing the cognitive state, further improvement is necessary for predicting progression. In this study, Random Forest and Bagging Decision Tree Recursive Feature Elimination (RFE) was utilized to ascertain the cognitive state and forecast progression. Clinical diagnoses, demographics, and post-processed PET and MRI scans used in this study were obtained from the Open Access Series of Imaging Studies (OASIS). The findings suggest that aging and lower levels of education are associated with higher risk. The study found that ML using post-processed MRI and PET scans, particularly RFE ML, is effective in diagnosing cognitive states with 90% accuracy. It can predict progression from CN to MCI or AD with 85% accuracy, which is significantly higher than the average reported in literature. Patients with progression from CN to AD were distinguished by elevated amyloid deposition, hippocampus and amygdala atrophy, left accumbens atrophy, thinning of the left hemisphere temporal, and enlarged inferior lateral ventricles. The study demonstrated that RFE ML is effective in diagnosing and predicting the progression of AD. Future studies will concentrate on identifying the specific regions of amyloid plaque that have the most significant impact on cognitive state and progression.  
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
Journal of Student Affairs Research and Practice
Journal of Student Affairs Research and Practice EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
2.40
自引率
9.10%
发文量
50
期刊介绍: The vision of the Journal of Student Affairs Research and Practice (JSARP) is to publish the most rigorous, relevant, and well-respected research and practice making a difference in student affairs practice. JSARP especially encourages manuscripts that are unconventional in nature and that engage in methodological and epistemological extensions that transcend the boundaries of traditional research inquiries.
×
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:481959085
Book学术官方微信