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. 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.