Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.
IEEE J Biomed Health Inform
; 25(3): 615-622, 2021 03.
Article
in English
| MEDLINE | ID: covidwho-1054464
ABSTRACT
A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism for defining specific operation regions associated to the behavior and uncertainty inherited to epidemiological data, and an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm for adaptive tracking and real time forecasting according to unobservable components computed by recursive spectral decomposition of experimental epidemiological data. Experimental results and comparative analysis illustrate the efficiency and applicability of proposed methodology for adaptive tracking and real time forecasting the dynamic propagation behavior of novel coronavirus 2019 (COVID-19) outbreak in Brazil.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Computer Simulation
/
Machine Learning
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
IEEE J Biomed Health Inform
Year:
2021
Document Type:
Article
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