Viral respiratory infections are the most common diseases suffered by all age groups worldwide. The gold standard for diagnosing viral respiratory infection is through the molecular method, but this diagnosis is expensive, requires sophisticated equipment, can only be performed by well-trained medical staff, and is painful. Volatile Organic Compounds (VOCs) are compounds released from the human body that can be a marker of disease and based on numerous studies it also contains VOCs. An electronic nose (E-nose) is a device that can be used to identify disease. This study proposes a new approach for the detection of viral respiratory infections through sweat from the armpit using an E-nose consisting of 5 semiconductor gases and a single-board computer. Several statistical parameters are used to obtain features and the detection algorithm used is Fully Connected Deep Network (FCDN). Several sizes of hidden layers were tested to obtain the best FCDN model. This study also proposes the selection of the best FCDN model which is a trade-off between complexity and accuracy, so that the model stored in E-nose is a model that not only has good accuracy but is also not too complex. The experimental results show that using 29 statistical parameters and 2 hidden layers generate the highest accuracy of 0.940 for the detection of 2 classes, namely positive and negative, with sensitivity and specificity of 0.967 and 0.915,

Original languageEnglish
Pages (from-to)394-404
Number of pages11
JournalInternational Journal of Intelligent Engineering and Systems
Issue number2
Publication statusPublished - Apr 2022


  • Armpit
  • Deep learning
  • Electronic nose
  • Statistical parameters
  • Sweat
  • Viral respiratory infections


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