Artificial intelligence techniques for predicting cardiorespiratory mortality caused by air pollution

Air pollution (AP) has risen as one of the biggest challenges of the 21st century, and it has adverse health effects for humans. The effects of health effects, including cardiorespiratory health effects of various air pollutants, are well documented. This research work presents the modeling and analysis of cardiorespiratory mortality attributed to AP. The modeling and predictions are also completed using four Artificial Intelligence (AI) techniques for comparison. The AI techniques utilized for comparison are (1) Enhanced Long Short-Term Memory (ELSTM), (2) Vector Autoregressive (VAR) (3) Deep Learning (DL), and (4) Long Short-Term Memory (LSTM). The research work is carried out at seven locations in Klang Valley, Malaysia. The five study locations i.e., Cheras, Petaling Jaya, Putrajaya, Shah Alam, and Klang have data from January 2006 to December 2016 and two relatively new monitoring stations, i.e., Banting and Batu Muda have data from April 2010 to December 2016 and January 2009 to December 2016, respectively. The comparison of results indicates that the ELSTM model predicts the cardiorespiratory mortality caused by AP significantly better than other AI models utilized in the study. The best Root-Mean-Squared Error (RMSE) results are obtained at Batu Muda and Klang study locations (ELSTM: 0.004, VAR: 0.03, DL: 0.0081, LSTM: 0.006) and (ELSTM: 0.005, VAR: 0.114, DL: 0.076, LSTM: 0.020), respectively. Based on the results, we can conclude that we can predict cardiorespiratory mortality based on air pollution in Klang Valley, Malaysia, using the AI techniques utilized in the study, especially ELSTM.