Citation

Aggarwal Y, Karan BM, Das BN, Aggarwal T, Sinha RK. J. Med. Syst. 2007; 31(6): 547-550.

Affiliation

Department of Biomedical Instrumentation, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835215, India. yogender.aggarwal@gmail.com

Copyright

(Copyright © 2007, Holtzbrinck Springer Nature Publishing Group)

DOI

unavailable

PMID

18041290

Abstract

Exertional heat illness is primarily a multi-system disorder results from the combined effect of exertional and thermoregulation stress. The severity of exertional heat illness can be classified as mild, intermediate and severe from non-specific symptoms like thirst, myalgia, poor concentration, hysteria, vomiting, weakness, cramps, impaired judgement, headache, diarrhea, fatigue, hyperventilation, anxiety, and nausea to more severe symptoms like exertional dehydration, heat cramps, heat exhaustion, heat injury, heatstroke, rhabdomyolysis, and acute renal failure. At its early stage, it is quite difficult to find out the severity of disease with manual screening because of overlapping of symptoms. Therefore, one need to classify automatically the disease based on symptoms. The 7:10:1 backpropagation artificial neural network model has been used to predict the clinical outcome from the symptoms that are routinely available to clinicians. The model has found to be effective in differentiating the different stages of exertional heat-illness with an overall performance of 100%.

Language: en