Early Detection of Sepsis using Machine Learning
S.V. Evangelin Sonia1, S. Sharanya2, M. Sivaram3, S. Vaishnavi4, S. Sakthidevi5
1S.V.Evangelin Sonia*, Assistant Professor, Computer Science and Ngineering Department, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
2S. Sharanya, Computer Science and Engineering Department, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
3M. Sivaram, Computer Science and Engineering Department, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
4S. Vaishnavi, Computer Science and Engineering Department, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
5S. Sakthidevi, Computer Science and Engineering Department, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India.
Manuscript received on April 02, 2020. | Revised Manuscript received on April 17, 2020. | Manuscript published on May 30, 2020. | PP: 489-493 | Volume-9 Issue-1, May 2020. | Retrieval Number: F9445038620/2020©BEIESP | DOI: 10.35940/ijrte.F9445.059120
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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 global cause of the death due to infection and subsequent overreaction of the immune system. Mortality rates are highest in developed and developing countries in cases of septic shock. Sepsis is a clinical condition with an emergency referral that can be avoided by advance warning. SIRS is a normal immune response to any infection, and hospitals or antibiotics are not required by most people. Early sepsis prediction is possibly life-saving, and we are planning to predict sepsis 6 hours before clinical sepsis diagnosis. We used two types of Standard Scalar and Min Max Scalar pre-treatment methods to pre-treat the data. The Recursive Feature Elimination (RFE) method is used to pick the features most closely related to the predictive or efficiency factor. The ML algorithms used are the Logistic Regression algorithm and X Gboost. Cross-validation 10 times with the train splitting method is used to validate attributes. we selected the best model from these two trained models.
Keywords: Sepsis, Machine Learning, Classifiers, Predictions.
Scope of the Article: Machine Learning