National Taiwan Ocean University Institutional Repository:Item 987654321/51194
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 28611/40652
造访人次 : 754441      在线人数 : 55
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 进阶搜寻

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51194

题名: Prediction of coronary artery disease based on ensemble learning approaches and co-expressed observations
作者: Ying-Tsang Lo
Hamido Fujita
Tun-Wen Pai
贡献者: 國立臺灣海洋大學:資訊工程學系
关键词: Coronary artery disease (CAD)
ensemble learning
co-expressed observation
TOPSIS
日期: 2016-02
上传时间: 2018-11-15T07:45:42Z
出版者: Journal of Mechanics in Medicine and Biology
摘要: Abstract: Background: Coronary artery disease (CAD) is one of the most representative cardiovascular diseases. Early and accurate prediction of CAD based on physiological measurements can reduce the risk of heart attack through medicine therapy, healthy diet, and regular physical activity. Methods:Four heart disease datasets from the UC Irvine Machine Learning Repository were combined and re-examined to remove incomplete entries, and a total of 822 cases were utilized in this study. Seven machine learning methods, including Naïve Bayes, artificial neural networks (ANNs), sequential minimal optimization (SMO), k-nearest neighbor (KNN), AdaBoost, J48, and random forest, were adopted to analyze the collected datasets for CAD prediction. By combining co-expressed observations and an ensemble voting mechanism, we designed and evaluated a new medical decision classifier for CAD prediction. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm was applied to determine the best prediction method for CAD diagnosis. Results: Features of systolic blood pressure, cholesterol, heart rate, and ST depression are considered to be the most significant differences between patients with and without CADs. We show that the prediction capability of seven machine learning classifiers can be enhanced by integrating combinations of observed co-expressed features. Finally, compared to the use of any single classifier, the proposed voting mechanism achieved optimal performance according to TOPSIS.
關聯: 16(1)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51194
显示于类别:[資訊工程學系] 期刊論文

文件中的档案:

档案 描述 大小格式浏览次数
index.html0KbHTML37检视/开启


在NTOUR中所有的数据项都受到原著作权保护.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈