Prediction of Chronic Obstructive Pulmonary Disease Stages Using Machine Learning Algorithms

Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The Fl score, specificity, sensitivity, accuracy, positive predictive value, and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients' records. The results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy, and hence, it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.