An associative memory approach to healthcare monitoring and decision making

Mario Aldape-Pérez; Antonio Alarcón-Paredes; Cornelio Yáñez-Márquez; Itzamá López-Yáñez; Oscar Camacho-Nieto

doi:10.3390/s18082690

An associative memory approach to healthcare monitoring and decision making

Mario Aldape-Pérez, Antonio Alarcón-Paredes, Cornelio Yáñez-Márquez, Itzamá López-Yáñez, Oscar Camacho-Nieto

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

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.

Original language	English
Article number	2690
Journal	Sensors (Switzerland)
Volume	18
Issue number	8
DOIs	https://doi.org/10.3390/s18082690
State	Published - 16 Aug 2018

Keywords

Associative memories
Decision support systems
Internet of Things
Pattern classification
e-Health

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10.3390/s18082690

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AU - Alarcón-Paredes, Antonio

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AU - López-Yáñez, Itzamá

AU - Camacho-Nieto, Oscar

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AB - 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.

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KW - Pattern classification

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An associative memory approach to healthcare monitoring and decision making

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Keywords

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