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Fulltext Machine learning methods to predict particulate matter PM 2.5

Palanichamy N, Haw SC, S S, Murugan R, Govindasamy K

F1000Res, 2022;11:406.
PMID: 36531254 DOI: 10.12688/f1000research.73166.1

Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.
Fulltext Prevalence and associated factors of suspected occupational skin diseases among restaurant workers in peninsular Malaysia: secondary data analysis of Registry for Occupational Disease Screening (RODS)

Ahmad Fuad MH, Samsudin EZ, Yasin SM, Ismail N, Mohamad M, Muzaini K, et al.

BMJ Open, 2024 Aug 13;14(8):e079877.
PMID: 39142678 DOI: 10.1136/bmjopen-2023-079877

OBJECTIVES: Occupational skin diseases (OSDs) pose significant risks to the health and well-being of restaurant workers. However, there is presently limited evidence on the burden and determinants of OSDs among this occupational group. This research aims to estimate the prevalence and associated factors of suspected OSDs among restaurant workers in Peninsular Malaysia.
DESIGN: A secondary data analysis of the 2023 Registry of Occupational Disease Screening (RODS) was performed. The RODS survey tool, which included the Nordic Occupational Skin Questionnaire, a symptoms checklist and items on work-relatedness, was used to screen for OSDs. Logistic regression analyses were performed to identify associated factors.
SETTING AND PARTICIPANTS: Restaurant workers (n=300) registered in RODS from February 2023 to April 2023, aged 18 years and above and working in restaurants across Selangor, Melaka and Pahang for more than 1 year, were included in the study, whereas workers who had pre-existing skin diseases were excluded.
RESULTS: The prevalence of suspected OSDs among study participants was 12.3%. Higher odds of suspected OSDs among study participants were observed among those exposed to wet work (adjusted OR (AOR) 22.74, 95% CI 9.63 to 53.68) and moderate to high job stress levels (AOR 4.33, 95% CI 1.80 to 10.43).
CONCLUSIONS: These findings suggest that OSDs are a significant occupational health problem among restaurant workers. Interventions targeting job content and wet work may be vital in reducing OSDs among this group of workers.

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