Non-Invasive Prediction Model to Detect Sepsis using Supervised Machine Learning Algorithms
Ruban S1, Elreena Maria Pinto2, Valerie Roselyn Cardozo3, Kavya S4
1Ruban S, Faculty Member PG, Department of IT, AIMIT, St Aloysius College, Mangaluru (Karnataka), India.
2Elreena Maria Pinto, PG Student, Department of IT, AIMIT, St Aloysius College, Mangaluru (Karnataka), India.
3Valerie Roselyn Cardozo, PG Student, Department of IT, AIMIT, St Aloysius College, Mangaluru (Karnataka), India.
4Kavya S, PG Student, Department of IT, AIMIT, St Aloysius College, Mangaluru (Karnataka), India.
Manuscript received on 13 February 2020 | Revised Manuscript received on 20 February 2020 | Manuscript Published on 28 February 2020 | PP: 50-52 | Volume-8 Issue-5S February 2020 | Retrieval Number: E10120285S20/2020©BEIESP | DOI: 10.35940/ijrte.E1012.0285S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sepsis is a life-threatening disease that causes tissue damage, organ failure and results in the death of millions of people. Sepsis is one of the highest risky diseases identified globally. A large proportion of these deaths occur in developing countries due to inaccessibility of hospitals or lack of resources. Blood samples are taken to confirm sepsis, but it requires the presence of laboratory and is time-consuming. The aim and objective of this study is to develop a practical, non-invasive sepsis prediction model that can be used to detect sepsis using supervised machine Learning algorithms. For this retrospective analysis, we used the data available from Physio-Net database.
Keywords: Sepsis, Prediction Model, Physio-Net Dataset, Non-Invasive.
Scope of the Article: Machine Learning