Predicting the morbidity of chronic obstructive pulmonary disease based on multiple locally weighted linear regression model with K-means clustering

Chronic Obstructive Pulmonary Disease (COPD) is a common chronic respiratory disease related to inflammation affected by harmful gas and particulate matter in the air. Mathematical prediction models between COPD and air pollutants are helpful for early identification, individualized interventions to slow disease progression, and for reduction of medical expenditures. The aim was to build a regression prediction model for the occurrence of COPD acute exacerbation. We collected hospital admissions for COPD in 2015-2018 from ten hospitals in Chongqing, China, used the increment per week as response, and the local sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matter 2.5 (PM2.5) concentrations as predictor variables to build a multiple prediction model. The Mean Absolute Percentage Error (MAPE) was used to evaluate the efficiency. We found that PM2.5 and SO2 are the most important factors contributing to the improvement of prediction accuracy. Multiple locally weighted linear regression (LWLR) Model based on integrated kernel framework with the K-means algorithm demonstrated minimum prediction error of 9.03 %(k=11).