COPD Prognosis under Biologically Inspired Neural Network

This paper proposes a prognostic model for rehabilitating the chronic obstructive pulmonary disease (COPD) patients in real time. The proposed approach applies a comprehensive predictive model employing a time series forecasting using condensed polynomial neural network with swarm intelligence. Disc...

Full description

Saved in:
Bibliographic Details
Published in2012 International Conference on Advances in Computing and Communications pp. 22 - 26
Main Authors Karuppanan, K., Vairasundaram, A. S., Sigamani, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2012
Subjects
Accuracy
Condensed Polynomial Neural Network
Genetic Algorithm
Genetic algorithms
Mathematical model
Particle swarm optimization
Polynomials
Sociology
Support vector classifier
Swarm Intelligence
Time Series Prediction
Online AccessGet full text
ISBN1467319112
9781467319119
DOI10.1109/ICACC.2012.6

Cover

More Information
Summary:This paper proposes a prognostic model for rehabilitating the chronic obstructive pulmonary disease (COPD) patients in real time. The proposed approach applies a comprehensive predictive model employing a time series forecasting using condensed polynomial neural network with swarm intelligence. Discrete particle swarm optimization (DPSO) filters out the relevant neurons and continuous particle swarm optimization (CPSO) reduces the computational overheads. The time series prediction is further strengthened by using multimodal genetic algorithm. Classification of the state of the patient is done by hybridized fuzzy C-means and support vectors. Control measures are applied meticulously to validate the predicted state of the patient.
ISBN:1467319112
9781467319119
DOI:10.1109/ICACC.2012.6