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    ASIA unversity > 資訊學院 > 資訊工程學系 > 博碩士論文 >  Item 310904400/112472


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112472


    Title: 利用運動心電圖數據預測重大冠狀動脈疾病的機器學習分類算法比較
    Comparison of Machine Learning Classification Algorithms to Predict Significant Coronary Artery Disease by Using Treadmill Exercise Data
    Authors: 林宇智
    LIN, YOU-ZHI
    Contributors: 資訊工程學系
    Keywords: 冠狀動脈疾病;運動心電圖;機器學習
    significant coronary artery disease;treadmill exercise;machine learning
    Date: 2019
    Issue Date: 2019-11-11 11:39:03 (UTC+8)
    Publisher: 亞洲大學
    Abstract: 近年來因心血管疾病而死亡的人數越來越多,是一個不能輕忽的疾病。心血管疾病在發病前,沒有特別明顯的症狀,常常被忽略。臨床上,醫生可以透過運動心電圖來檢查病人是否有心血管疾病。運動心電圖是讓病人於跑步機上行走,記錄其運動狀態的心電圖及血壓和心跳數。文獻上,醫生利用運動心電圖診斷心血管疾病的準確率大約落在70~75%。在本論文中,研究使用各種機器學習分類演算法進行運動心電圖數據的分析。實驗數據是100名懷疑和已知冠狀動脈疾病的患者數據,這些患者經過醫生診斷後接受運動心電圖檢查。本研究使用五種機器學習分類算法:最近鄰居法、決策樹、隨機森林、支持向量機和極限梯度提升。實驗結果,極限梯度提升分類器在所有分類模型具有最高的性能表現,可以達到84%的正確率。結果顯明,機器學習方法可以用來輔助醫生提高心血管疾病診斷的準確率。
    In recent years, the number of people dying from cardiovascular diseases is increasing, and it is a disease that cannot be ignored. Cardiovascular disease has no particularly obvious symptoms before onset and is often overlooked. Clinically, doctors can check whether a patient has cardiovascular disease through a sports ECG. The exercise ECG is an electrocardiogram and blood pressure and heart rate that allow the patient to walk on the treadmill and record their movement status. In the literature, the accuracy of doctors using cardiovascular electrocardiography to diagnose cardiovascular disease falls to about 70-75%. In this paper, we study the use of various machine learning classification algorithms for the analysis of exercise ECG data. The experimental data is data from 100 patients with suspected and known coronary artery disease who underwent exercise electrocardiography after a doctor's diagnosis. We use five machine learning classification algorithms: K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest(RF), Support Vector Machine(SVM) and eXtreme Gradient Boosting (XGBboost). As a result of the experiment, the XGBboost classifier has the highest performance in all classification models and can achieve an accuracy rate of 84%. The results show that machine learning methods can be used to assist doctors in improving the accuracy of cardiovascular disease diagnosis.
    Appears in Collections:[資訊工程學系] 博碩士論文

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