School of Computer Science and Engineering, Taylor's University, Subang Jaya, Selangor, Malaysia.
College of Computing and Informatics, Department of Computer Science, University of Sharjah, 27272, Sharjah, United Arab Emirates.
Environ Sci Pollut Res Int. 2021 Oct;28(40):56759-56771. doi: 10.1007/s11356-021-14305-7. Epub 2021 Jun 1.
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. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. 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., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. 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.
空气污染对人类健康有严重的不良影响，已成为全球范围内人类福祉和健康的风险因素。空气污染对健康的主要影响之一是与空气污染相关的住院治疗。最近，使用人工智能 (AI) 和机器学习 (ML) 技术，即深度学习和长短期记忆 (LSTM)，对基于空气污染的住院治疗进行了估计和预测。然而，在现有的应用方法中，仍有很大的改进空间，可以用来估计和预测基于空气污染的住院人数。在本文中，我们对空气污染和心肺住院进行了建模和分析。本研究旨在探讨心肺住院与空气污染之间的关系，并使用人工智能 (AI) 技术基于空气污染预测心肺住院。我们提出了增强型长短期记忆 (ELSTM) 模型，并与其他 AI 技术，即 LSTM、DL 和向量自回归 (VAR) 进行了比较。本研究在马来西亚雪兰莪州八打灵再也的七个研究地点进行。所使用的数据集包含 2006 年 1 月至 2016 年 12 月的五个研究地点的数据，即巴生 (KLN)、沙阿兰 (SA)、布城 (PUJ)、八打灵再也 (PJ) 和蕉赖，吉隆坡 (CKL)。班亭的数据包含 2010 年 4 月至 2016 年 12 月的数据，而吉隆坡武吉毛律的数据包含 2009 年 1 月至 2016 年 12 月的数据。预测结果表明，ELSTM 模型在所有研究地点的表现都明显优于其他模型，KLN 研究地点的 RMSE 得分最好 (ELSTM：0.002，LSTM：0.013，DL：0.006，VAR：0.066)。结果还表明，与 LSTM 和其他模型相比，所提出的 ELSTM 模型能够更好地检测和预测研究中每月住院的趋势。因此，我们可以得出结论，我们可以利用人工智能技术准确预测马来西亚雪兰莪州八打灵再也的心肺住院人数。
