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Open AccessArticle

An Associative Memory Approach to Healthcare Monitoring and Decision Making

1
Instituto Politécnico Nacional, Computational Intelligence Laboratory at CIDETEC, Ciudad de Mexico 07700, Mexico
2
Universidad Autónoma de Guerrero, Engineering Department, Guerrero 39079, Mexico
3
Instituto Politécnico Nacional, Computational Intelligence Laboratory at CIC, Ciudad de Mexico 07738, Mexico
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(8), 2690; https://doi.org/10.3390/s18082690
Received: 17 June 2018 / Revised: 4 August 2018 / Accepted: 14 August 2018 / Published: 16 August 2018
(This article belongs to the Special Issue Sensor Applications in Medical Monitoring and Assistive Devices)
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. View Full-Text
Keywords: associative memories; decision support systems; e-Health; Internet of Things; pattern classification associative memories; decision support systems; e-Health; Internet of Things; pattern classification
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Aldape-Pérez, M.; Alarcón-Paredes, A.; Yáñez-Márquez, C.; López-Yáñez, I.; Camacho-Nieto, O. An Associative Memory Approach to Healthcare Monitoring and Decision Making. Sensors 2018, 18, 2690.

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