Cardiorespiratory Mortality Prediction Based on Air Pollution Using Tree-Based Ensemble Models

Abstract

Air pollution has a substantial negative impact on human wellbeing and health. Cardiorespiratory mortality is one of the primary effects of air pollution. In this study, we provide analysis of air pollution, cardiorespiratory mortality and the cardiorespiratory mortality is predicted based on air pollution using tree-based ensemble models. The tree-based ensemble models utilized in this study are Voting Regressor (VR), Random Forest (RF), Gradient Tree Boosting (GB), and Extreme Gradient Boosting (XGBoost). The used dataset contains data for five research locations: Shah Alam (SA), Klang (KLN), Putrajaya (PUJ), Cheras, Kuala Lumpur (CKL), and Petaling Jaya (PJ) from January 2006 to December 2016. The results show that XGBoost and VR models outperformed the rest of the models with the best evaluation metric scores in the Klang study area, XGBoost(MAE:0.005, RMSE:0.010, MAPE:0.70%) and VR (MAE:0.005, RMSE:0.011, MAPE:0.70%). The results reveal that the utilized models provided an excellent and accurate prediction of cardiorespiratory mortality based on air pollution and can follow the trend of cardiorespiratory mortality.