Detection of Sepsis using Machine Learning Approach

  • T. Balachander, Snehil Sharma, Amrit Srivastava

Abstract

Sepsis which is also known as Silent killer, is a life- minatory condition that results from both infection and body’s reaction to infection, eventually creating injury to ones tissues and   organs, and finally leads to death.. The main disadvantage is that there is much delay in detecting the diseases and identifying the different stages. Normally  this is done with culture test. It may takes two or more than two days to get the desired output. By that  time the organs start to become abnormal. Children, particularly new born and young infants, have more chances of developing Sepsis. It is not an easy job  to detect the disease. .In subsisting system, the medical teams determine the disease from the patient’s  previous medical records, symptoms, a physical exam and test. It’s impossible to prevent Sepsis. But preventing infection can decrease the occurence of Sepsis.so as to maintain a strategic distance from the defer we use machine learning idea. it is a technique used to comprehend complex models and calculations that loan themselves to forecast. early discovery of sepsis can enhance tolerant results and lessen social insurance cost.

Keywords: Neonatal intensive care unit( NICU), Machine Learning(ML) Sci-Kit learn(sklearn).

Published
2020-05-06
How to Cite
T. Balachander, Snehil Sharma, Amrit Srivastava. (2020). Detection of Sepsis using Machine Learning Approach. International Journal of Advanced Science and Technology, 29(06), 2651 - 2658. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/13726