Name
Abstract | ||
|---|---|---|
The rapid proliferation of connectivity, availability of ubiquitous computing, miniaturization of sensors and communication technology, have changed healthcare in all its areas, creating the well-known healthcare paradigm of e-Health. In this paper, an embedded system capable of monitoring, learning and classifying biometric signals is presented. The machine learning model is based on associative memories to predict the presence or absence of coronary artery disease in patients. Classification accuracy, sensitivity and specificity results show that the performance of our proposal exceeds the performance achieved by each of the fifty widely known algorithms against which it was compared. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18082690 | SENSORS |
Keywords | Field | DocType |
associative memories,decision support systems,e-Health,Internet of Things,pattern classification | Health care,Content-addressable memory,Electronic engineering,Human–computer interaction,Engineering | Journal |
Volume | Issue | Citations |
18 | 8.0 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario Aldape-Pérez | 1 | 63 | 8.78 |
| Antonio Alarcón Paredes | 2 | 6 | 2.65 |
| Cornelio Yáñez-Márquez | 3 | 153 | 26.34 |
| Itzamá López-Yáñez | 4 | 78 | 11.76 |
| Oscar Camacho Nieto | 5 | 65 | 14.93 |