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A Web Based Cardiovascular Disease Detection System.

Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demograp...

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Published in:Journal of Medical Systems Vol. 39; no. 10; pp. 1 - 7
Main Authors: Alshraideh, Hussam, Otoom, Mwaffaq, Al-Araida, Aseel, Bawaneh, Haneen, Bravo, José
Format: research tables/charts tracings Journal Article
Published: Springer Nature Oct2015
Online Access:View this record in EBSCOhost
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      dt: Oct2015
      vid: 39
      iid: 10
      pid: 237
      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-015-0290-7
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        atl: A Web Based Cardiovascular Disease Detection System.
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        au:
          Alshraideh, Hussam
          Otoom, Mwaffaq
          Al-Araida, Aseel
          Bawaneh, Haneen
          Bravo, José
        affil: Department of Industrial Engineering, Jordan University of Science and Technology, Irbid Jordan
      sug:
        subj:
          Cardiovascular Diseases Diagnosis
          Electrocardiography
          Early Diagnosis
          Algorithms
          Telehealth
          Systems Design
          Descriptive Statistics
          Decision Trees
          Cardiovascular Diseases Classification
          Validity
          Heart Conduction System
          Data Mining
          QRS Complex
          Neural Networks (Computer)
          Arrhythmia Diagnosis
          California
          Arrhythmia Classification
          Cloud Computing
          Smartphone
          Mobile Applications
          Text Messaging
          User-Computer Interface
          Self Care
          Health Status
      ab: Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29 % accuracy for the decision tree classification algorithm. The algorithm has been integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        tracings
        Journal Article
      ougenre: Article
    language: English
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