Analysis of spirometric pulmonary function test using support vector regression and classification

Abstract

In this work investigations are carried out on enhancing the newlinediagnostic relevance of spirometric measurements using support vector newlineregression and classification The pulmonary function data are recorded from newlinevolunteers N225 using flow volume spirometer and a standard data newlineacquisition protocol The prediction of most significant parameters such as newlineForced Expiratory Volume in 1 second FEV1 and 6 seconds FEV6 are newlinecarried out from the recorded dataset using support vector regression The newlineeffect of prediction of significant parameters in spirometric transducer newlineresistance is analyzed using error factor in forced expiratory volume newline newline