Prediction of FEV1 and FEV6 in normal and obstructive abnormality using ELM regression and spirometric investigations

Spirometric pulmonary function test is a well-established test in clinical medicine for the assessment of respiratory diseases. It measures the volume of air inhaled or exhaled as a function of time during forced breathing maneuvers and generates large data set. However, spirometric investigation is often prone to incomplete data sets due to inability of the children and patient to perform this test. Hence, there is a requirement for prediction of significant parameters from incomplete data set. In this work, Extreme Learning Machines (ELM) based prediction of significant parameters which include forced expiratory volumes at first second and sixth seconds are employed. ELM with radial basis function and sine activation functions is used to train and test the prediction process. The performance of the regression model is validated with prediction accuracy and root mean squared error. Results demonstrate that ELM with Radial Basis Function (RBF) achieves higher prediction accuracy (98%) for FEV₁ prediction with 20 hidden neurons. Prediction accuracy of 95% is obtained for FEV₆ with sine activation function. Further, it is observed that less prediction error and high correlation are obtained in FEV₁ prediction. It appears that this method of prediction of significant parameter could be clinically relevant in the diagnosis of pulmonary abnormalities with incomplete data set and missing values.