Sepsis Detection in newborn infants - Diagnosis using fuzzy inference system- A Review

Authors

  • MS Kalas Dept. of Computer Science and Engg, KIT’S College of Engineering (Autonomous), Kolhapur, Maharashtra, India
  • Nikita D. Deshpande Dept. of Computer Science and Engg, KIT’S College of Engineering (Autonomous), Kolhapur, Maharashtra, India

DOI:

https://doi.org/10.26438/ijcse/v9i5.4346

Keywords:

Disease, diagnosis, Sepsis Detection, Fuzzy logic

Abstract

The detection of a health problem, illness, disability, or other condition that an individual may have is known as disease diagnosis. Large data sets are available; however, the tools that can accurately evaluate trends and make predictions are limited. Traditional methods of diagnosing diseases are considered to be not effective in getting accuracy and prone to error. Artificial Intelligence (AI) is being used to forecast the future. AI with predictive techniques enables to provide auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper we have taken review of sepsis detection in newborn infants using techniques of AI, like Fuzzy Logic and identified limitations of these studies. The aim of this research paper is to reveal some key insights into medical techniques. Based on a series of open problems and challenges, the paper also suggests some directions for potential research on AI-based diagnostics systems.

References

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Published

2021-05-31
CITATION
DOI: 10.26438/ijcse/v9i5.4346
Published: 2021-05-31

How to Cite

[1]
M. Kalas and N. D. Deshpande, “Sepsis Detection in newborn infants - Diagnosis using fuzzy inference system- A Review”, Int. J. Comp. Sci. Eng., vol. 9, no. 5, pp. 43–46, May 2021.

Issue

Vol. 9 No. 5 (2021): IJCSE May Edition

Section

Review Article

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