An Associative Memory Approach to Healthcare Monitoring and Decision Making

Sensors

Cornelio Yáñez-Márquez¹  Oscar Camacho-Nieto²  Mario Aldape-Pérez²  Itzamá López-Yáñez²  Antonio Alarcón-Paredes³ 

[1] Instituto Politécnico Nacional, Computational Intelligence Laboratory at CIC, Ciudad de Mexico 07738, Mexico;Instituto Politécnico Nacional, Computational Intelligence Laboratory at CIDETEC, Ciudad de Mexico 07700, Mexico;Universidad Autónoma de Guerrero, Engineering Department, Guerrero 39079, Mexico;

关键词: associative memories;    decision support systems;    e-Health;    Internet of Things;    pattern classification;   

DOI  :  10.3390/s18082690

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown