Affiliations: [a] Dhanalakshmi College of Engineering, Dr VPR Nagar, Manimangalam, Tambaram, Chennai, Tamil Nadu, India | [b] SSN College of Engineering, Kalavakkam, Tamil Nadu, India
Correspondence:
[*]
Corresponding author: Mahesh Veezhinathan, SSN College of Engineering, India. E-mail:[email protected]
Abstract: Spirometry evaluates the integrated function of lung
capacity and chest wall mechanics measuring the total volume of air
forcefully exhaled from a fully inflated lung. This non-invasive,
informative technique for characterizing pulmonary function has an important
role in clinical trials to narrow the differential diagnosis of subjects
being assessed for pulmonary disorders. The test however requires patient
co-operation and sometimes sub maximal effort affects the results
potentially thereby leading to incomplete test and misdiagnosis. The aim of
this work is to develop a prediction model based on Multivariate adaptive
regression splines (MARS) technique to estimate the spirometric parameter
Peak Expiratory Flow (PEF) volume. In the present study, flow-volume data
from N = 220 subjects are considered. Model performances are evaluated
statistically with coefficient of determination (R2) and Root Mean
Squared Error (RMSE). The significant spirometric features captured in the
model were FEV1, FEF50, FEF25 and the demographic parameter
weight. Bland-Altman plots for the estimated PEF values showed a minimal
bias. The MARS model successfully adopted the important features for
prediction of PEF parameter with overall good fit and these findings can
assist clinicians with enhanced spirometric investigations on respiratory
disorders.
