Individualized prediction of heat stress in firefighters: a data-driven approach using classification and regression trees

Details

• Personal Author:
  Bhattacharya, Amit ; James K ; Mani A ; Rao M
• Description:
  The purpose of this study was to explore data driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38 OC) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters. [Description provided by NIOSH]
• Subjects:
  Aging Body Temperature Body Weight Cardiovascular System Firefighters Heat Stress Disorders Hot Temperature Hypothermia Occupational Health Safety Staff Development

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• Keywords:
  Age-groups Author Keywords: Firefighters Body-temperature Body-weight Classification Tree Data Driven Models Decision Tree Fire-fighters Fire-fighting Heart-rate Heat-stress Heat Stress Hyperthermia Models Physiological-effects Physiological-factors Physiology Prediction Regression Tree Statistical-analysis Training

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• ISSN:
  1545-9624
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  12
• Issue:
  12
• NIOSHTIC Number:
  nn:20046434
• Citation:
  J Occup Environ Hyg 2015 Dec; 12(12):845-854
• Email:
  bhattaat@ucmail.uc.edu
• Federal Fiscal Year:
  2016
• Performing Organization:
  University of Cincinnati
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Journal of Occupational and Environmental Hygiene
• End Date:
  20260630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:efef55f4d7b9794a1778d90aacefd3be0b1ab1813a095dac207635de0fe0fe58d8b98191e36cfb7e15c062731b3222cab60fb915610842015f85fd31a9bf89fe
• Download URL:
  https://stacks.cdc.gov/view/cdc/201494/cdc_201494_DS1.pdf
• File Type:
  [PDF - 925.57 KB ]