Abstract：
:In this work, prediction of forced expiratory volume in pulmonary function test, carried out using spirometry and neural networks is presented. The pulmonary function data were recorded from volunteers using commercial available flow volume spirometer in standard acquisition protocol. The Radial Basis Function neural networks were used to predict forced expiratory volume in 1 s (FEV1) from the recorded flow volume curves. The optimal centres of the hidden layer of radial basis function were determined by k-means clustering algorithm. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal, restrictive and obstructive cases. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases. Prediction accuracy was more in obstructive abnormality when compared to restrictive cases. It appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.
journal_name
J Med Systjournal_title
Journal of medical systemsauthors
Manoharan SC,Ramakrishnan Sdoi
10.1007/s10916-008-9196-ysubject
Has Abstractpub_date
2009-10-01 00:00:00pages
347-51issue
5eissn
0148-5598issn
1573-689Xjournal_volume
33pub_type
杂志文章abstract：:The Department of Defense has been engaged in the Defense Modeling and Simulation Initiative (DMSI) to provide advanced distributed simulation warfighters in geographically distributed localities. Lessons learned from the Defense Simulation Internet (DSI) concerning architecture, standards, protocols, interoperability...
journal_title：Journal of medical systems
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journal_title：Journal of medical systems
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