Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

Abstract

Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the Artificial Intelligence (AI) techniques. We propose the Enhanced Long Short-Term Memory (ELSTM) model and provide a comparison with other AI techniques, i.e., Long Short-Term Memory (LSTM), Deep Learning (DL), and Vector Autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

COVID-19 Cases Prediction in Saudi Arabia Using Tree-based Ensemble Models

“…The limitations of the study includes only using tree-based ensemble models and limited COVID-19 dataset, as the pandemic is still affecting the world. In the future work, we would like to compare the tree-based ensemble models with predictive and time series models such as Neural Networks [58] and LSTMs [59].…”