| 電子情報通信学会 研究会発表申込システム 講演論文 詳細 |
技報閲覧サービス [ログイン] 技報アーカイブ |
| トップに戻る | 前のページに戻る | [Japanese] / [English] |
| 講演抄録／キーワード | ||
| 講演名 | 2008-09-22 09:00 ［招待講演］A Data Mining Approach and Framework of Intelligent Diagnosis System for Coronary Artery Disease Prediction ○Keun Ho Ryu（Chungbuk National Univ.）・Wuon Shik Kim（Korea Research Inst. of Standards and Science）・Heon Gyu Lee（Chungbuk National Univ.）  DE2008-46 |
|
| 抄録 | （和） | One of our work is to develop a new and unique methodology useful in developing the various features of heart rate variability (HRV) and carotid arterial wall thickness helpful in diagnosing cardiovascular disease. In the paper, we not only propose a framework of intelligent diagnosis system but also describe a suitable prediction model to enhance the reliability of medical examinations and treatments for cardiovascular disease. We analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on HRV indexes. 99 normal subjects and 94 patients with coronary artery disease participated in this study. In order to extract various features, we tested six classification methods. As a result, CPAR and SVM (gave about 85%-90% goodness of fit) outperformed the other classifiers. |
| （英） | One of our work is to develop a new and unique methodology useful in developing the various features of heart rate variability (HRV) and carotid arterial wall thickness helpful in diagnosing cardiovascular disease. In the paper, we not only propose a framework of intelligent diagnosis system but also describe a suitable prediction model to enhance the reliability of medical examinations and treatments for cardiovascular disease. We analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on HRV indexes. 99 normal subjects and 94 patients with coronary artery disease participated in this study. In order to extract various features, we tested six classification methods. As a result, CPAR and SVM (gave about 85%-90% goodness of fit) outperformed the other classifiers. | |
| キーワード | （和） | Coronary Artery Disease / Heart Rate Variability / Carotid Arterial Wall Thickness / Intelligent Diagnosis System / Data Mining / / / |
| （英） | Coronary Artery Disease / Heart Rate Variability / Carotid Arterial Wall Thickness / Intelligent Diagnosis System / Data Mining / / / | |
| 文献情報 | 信学技報, vol. 108, no. 211, DE2008-46, pp. 33-34, 2008年9月. | |
| 資料番号 | DE2008-46 | |
| 発行日 | 2008-09-14 (DE) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | DE2008-46 |
|
[研究会発表申込システムのトップページに戻る]