Name
Abstract | ||
|---|---|---|
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 patientu0027s body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10916-015-0290-7 | Journal of Medical Systems |
Keywords | Field | DocType |
Cardiovascular, Classification, Electrocardiography, WEKA | Decision tree,Signal processing,Data mining,Early detection,Disease,Android (operating system),Web application,Medicine,The Internet,Cloud computing | Journal |
Volume | Issue | ISSN |
39 | 10 | 1573-689X |
Citations | PageRank | References |
5 | 0.42 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hussam Alshraideh | 1 | 24 | 3.74 |
| Mwaffaq Otoom | 2 | 38 | 6.80 |
| Aseel Al-Araida | 3 | 5 | 0.42 |
| Haneen Bawaneh | 4 | 5 | 0.42 |
| José Bravo | 5 | 488 | 48.08 |