Name
Abstract | ||
|---|---|---|
•Describes a predictive analytics example of interpretation of sensor data in healthcare.•Created multi-label classification model to classify five heart conditions.•Trained and tested models on more than 6800 hand annotated EKGs.•The best model achieves a multi-label classification accuracy of 91%.•Our model could help alert the users about their cardiac states through smartphones. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2018.12.056 | Expert Systems with Applications |
Keywords | Field | DocType |
Multi-label classification,Machine learning,EKG sensor,Smartphone app,Predictive analytics | Atrial fibrillation,USable,Data mining,Atrioventricular block,Computer science,Expert system,Artificial intelligence,Chart,Sinus bradycardia,Artificial neural network,Analytics,Machine learning | Journal |
Volume | ISSN | Citations |
123 | 0957-4174 | 0 |
PageRank | References | Authors |
0.34 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pankush Kalgotra | 1 | 4 | 1.82 |
| Ramesh Sharda | 2 | 1063 | 98.91 |
| Bryan I. Hammer | 3 | 28 | 2.86 |
| David E. Albert | 4 | 0 | 0.34 |