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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence

Raja Sher Afgun Usmani1, Thulasyammal Ramiah Pillai2, Ibrahim Abaker Targio Hashem3

  • 1School of Computer Science and Engineering, Taylor's University, Subang Jaya, Selangor, Malaysia. rajasherafgunusmani@sd.taylors.edu.my.

Environmental Science and Pollution Research International
|June 2, 2021
PubMed
Summary

Air pollution significantly impacts health, leading to hospitalizations. An enhanced long short-term memory (ELSTM) model accurately predicts cardiorespiratory hospitalizations linked to air pollution in Malaysia.

Keywords:
Air pollutionAir qualityHealthHospital admissionsHospitalizationMachine learningPrediction

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Area of Science:

  • Environmental health
  • Public health
  • Artificial Intelligence in healthcare

Background:

  • Air pollution poses a global risk to human health, with hospitalizations being a major consequence.
  • Existing artificial intelligence (AI) and machine learning (ML) methods for predicting air pollution-related hospitalizations require improvement.

Purpose of the Study:

  • To investigate the association between air pollution and cardiorespiratory hospitalizations.
  • To predict cardiorespiratory hospitalizations using AI techniques based on air pollution data.
  • To introduce and evaluate an enhanced long short-term memory (ELSTM) model against other AI methods.

Main Methods:

  • The study utilized air pollution and cardiorespiratory hospitalization data from seven locations in Klang Valley, Malaysia (2006-2016).
  • An enhanced long short-term memory (ELSTM) model was developed and compared with long short-term memory (LSTM), deep learning (DL), and vector autoregressive (VAR) models.
  • Model performance was evaluated using Root Mean Square Error (RMSE) and trend prediction accuracy.

Main Results:

  • The ELSTM model demonstrated superior performance in predicting cardiorespiratory hospitalizations across all study locations compared to LSTM, DL, and VAR.
  • Klang study location showed the best performance with the ELSTM model achieving an RMSE of 0.002.
  • The ELSTM model effectively captured and predicted monthly hospitalization trends, outperforming other models.

Conclusions:

  • AI techniques, particularly the proposed ELSTM model, can accurately predict cardiorespiratory hospitalizations associated with air pollution in Klang Valley, Malaysia.
  • The findings highlight the potential of advanced AI models for public health surveillance and intervention strategies related to air quality.