Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease
Our aim was to develop automatic classifiers to help the categorisation of airway obstruction level in patients with chronic obstructive pulmonary disease (COPD). We used different techniques, including k-nearest neighbour (KNN), random forest (RF) and support vector machines with linear and radial basis function kernels. Feature selection methods were also used to improve the performance of the classifiers. Our findings revealed that KNN and RF classifiers improved categorisation accuracy. Our study and findings will contribute to improvement of tracking COPD progression and guiding therapy.
airway obstruction severity chronic obstructive pulmonary disease clinical decision support classification artificial intelligence forced oscillation technique