Artificial Neural Network Application for Sepsis Prediction: A Preliminary Study


  • AK Faizin
  • W Saputro
  • RD Issafira
  • L Edahwati
  • WD Lestari
  • TP Sari
  • N Adyono



Sepsis, Artificial Neural Network, Discrete Wavelet Transform, Hilbert-Huang Transform.


Sepsis is one of the leading causes of mortality in hospitalized patients. It is very difficult to find the symptoms of sepsis because of their similarity to the symptoms of other diseases. This paper aims to deliver an artificial neural network implementation in medical decisions support. This study tries to predict sepsis and healthy patient based on vital signs such as heart rate, systolic blood pressure, and diastolic blood pressure taken from the MIMIC-III clinical database. There were several extraction processes applied to vital sign signals such as using the statistical tools, discrete wavelet transforms, and Hilbert-Huang Transform. The ANN algorithm predicts the sepsis patient with 96.7% of accuracy. However, based on the medical requirement for artificial intelligent implementation, this result does not satisfy the requirement as the false positive error is 2.9%.   




How to Cite

AK Faizin, W Saputro, RD Issafira, L Edahwati, WD Lestari, TP Sari, & N Adyono. (2023). Artificial Neural Network Application for Sepsis Prediction: A Preliminary Study. BIOMEJ, 2(2), 45–50.




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