東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Prediction of Coronary Heart Disease Using Metabolite-Based Machine Learning Models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prediction of Coronary Heart Disease Using Metabolite-Based Machine Learning Models./
作者:	Zhou, Xintong.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	48 p.
附註:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28544657
ISBN:	9798516962677

Prediction of Coronary Heart Disease Using Metabolite-Based Machine Learning Models.
Zhou, Xintong.

Prediction of Coronary Heart Disease Using Metabolite-Based Machine Learning Models. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 48 p.

Source: Masters Abstracts International, Volume: 83-01.

Thesis (M.S.)--University of California, San Diego, 2021.

This item must not be sold to any third party vendors.

Coronary heart disease (CHD) is a leading cause of death in the United States. Currently, the main method of risk assessment is carried out through established risk score algorithms by using traditional risk factors. These algorithms mainly focus on long-term prediction, with the limitation on assessing risk for younger adults. In recent years, with the advancement of serum nuclear magnetic resonance (NMR), more studies of using metabolites to predict CHD have merged. Assessing the risk with metabolites provides insights into the underlying molecular mechanisms of CHD. This thesis explores that possibility of using metabolites as the predictors and is aiming to understand how much prediction power that machine learning methods could bring in this prediction task.

ISBN: 9798516962677Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Coronary heart disease

Prediction of Coronary Heart Disease Using Metabolite-Based Machine Learning Models.
LDR:02004nmm a2200409 4500 001 2345971
005 20220613064827.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798516962677
035 $a (MiAaPQ)AAI28544657
035 $a AAI28544657
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhou, Xintong. $3 3684989
245 1 0 $a Prediction of Coronary Heart Disease Using Metabolite-Based Machine Learning Models.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 48 p.
500 $a Source: Masters Abstracts International, Volume: 83-01.
500 $a Advisor: Rao, Ramesh;Jain, Mohit.
502 $a Thesis (M.S.)--University of California, San Diego, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Coronary heart disease (CHD) is a leading cause of death in the United States. Currently, the main method of risk assessment is carried out through established risk score algorithms by using traditional risk factors. These algorithms mainly focus on long-term prediction, with the limitation on assessing risk for younger adults. In recent years, with the advancement of serum nuclear magnetic resonance (NMR), more studies of using metabolites to predict CHD have merged. Assessing the risk with metabolites provides insights into the underlying molecular mechanisms of CHD. This thesis explores that possibility of using metabolites as the predictors and is aiming to understand how much prediction power that machine learning methods could bring in this prediction task.
590 $a School code: 0033.
650 4 $a Electrical engineering. $3 649834
650 4 $a Biomedical engineering. $3 535387
650 4 $a Biostatistics. $3 1002712
650 4 $a Artificial intelligence. $3 516317
650 4 $a Health care management. $3 2122906
650 4 $a Diabetes. $3 544344
650 4 $a Accuracy. $3 3559958
650 4 $a Regression analysis. $3 529831
650 4 $a Body mass index. $3 3562858
650 4 $a Nuclear magnetic resonance--NMR. $3 3560258
650 4 $a Risk factors. $3 3543864
650 4 $a Cholesterol. $3 3382101
650 4 $a Survival analysis. $3 3566266
650 4 $a Bias. $2 gtt $3 1374837
650 4 $a Principal components analysis. $3 565921
650 4 $a Age. $3 1486010
650 4 $a Blood pressure. $3 661055
650 4 $a Decision making. $3 517204
650 4 $a Hypertension. $3 825110
650 4 $a Classification. $3 595585
650 4 $a Support vector machines. $3 2058743
650 4 $a Cardiovascular disease. $3 3564561
650 4 $a Veins & arteries. $3 3681939
650 4 $a Metabolic syndrome. $3 898683
650 4 $a Algorithms. $3 536374
650 4 $a Decision trees. $3 827433
650 4 $a Metabolites. $3 683644
650 4 $a Variance analysis. $3 3544969
653 $a Coronary heart disease
653 $a Coronary arterydisease
653 $a Risk
653 $a Younger adults
653 $a Predictors
653 $a Machine learning
690 $a 0544
690 $a 0541
690 $a 0800
690 $a 0769
690 $a 0308
710 2 $a University of California, San Diego. $b Electrical and Computer Engineering. $3 3432690
773 0 $t Masters Abstracts International $g 83-01.
790 $a 0033
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28544657