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 2015-10, Vol.39 (10), p.122-122, Article 122
Main Authors: Alshraideh, Hussam, Otoom, Mwaffaq, Al-Araida, Aseel, Bawaneh, Haneen, Bravo, José
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Arrhythmias, Cardiac - diagnosis
Cardiac arrhythmia
Cardiovascular disease
Cardiovascular Diseases - diagnosis
Cardiovascular system
Classification
Cloud Computing
Clustering
Data Mining
Decision Trees
Demographics
Electrocardiography
Electrocardiography - instrumentation
Health
Health Informatics
Health Sciences
Heart
Humans
Internet
Literature reviews
Medicine
Medicine & Public Health
Monitoring, Ambulatory - instrumentation
Neural networks
Patient Facing Systems
Patients
Signal processing
Signal Processing, Computer-Assisted - instrumentation
Smartphone
Socioeconomic Factors
Statistics for Life Sciences
Support vector machines
UCAmI & IWAAL 2014
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cited_by cdi_FETCH-LOGICAL-c405t-685d25ba803da4850dd3a574867ce731a65f92ac46eac62e2328dea4096a6bc3
cites cdi_FETCH-LOGICAL-c405t-685d25ba803da4850dd3a574867ce731a65f92ac46eac62e2328dea4096a6bc3
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container_issue 10
container_start_page 122
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creator Alshraideh, Hussam
Otoom, Mwaffaq
Al-Araida, Aseel
Bawaneh, Haneen
Bravo, José
description 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.
doi_str_mv 10.1007/s10916-015-0290-7
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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. 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identifier ISSN: 0148-5598
ispartof Journal of medical systems, 2015-10, Vol.39 (10), p.122-122, Article 122
issn 0148-5598
1573-689X
language eng
recordid cdi_proquest_miscellaneous_1808065817
source Springer Nature
subjects Accuracy
Algorithms
Arrhythmias, Cardiac - diagnosis
Cardiac arrhythmia
Cardiovascular disease
Cardiovascular Diseases - diagnosis
Cardiovascular system
Classification
Cloud Computing
Clustering
Data Mining
Decision Trees
Demographics
Electrocardiography
Electrocardiography - instrumentation
Health
Health Informatics
Health Sciences
Heart
Humans
Internet
Literature reviews
Medicine
Medicine & Public Health
Monitoring, Ambulatory - instrumentation
Neural networks
Patient Facing Systems
Patients
Signal processing
Signal Processing, Computer-Assisted - instrumentation
Smartphone
Socioeconomic Factors
Statistics for Life Sciences
Support vector machines
UCAmI & IWAAL 2014
title A Web Based Cardiovascular Disease Detection System
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