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Early Prediction of Sepsis Using Machine Learning

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Volume-10 | Issue-4

Last date : 27-Aug-2026

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Early Prediction of Sepsis Using Machine Learning


Er. Yasmeen Viqar | Furqaan | Mehak Ayoub | Toiba Tahir



Er. Yasmeen Viqar | Furqaan | Mehak Ayoub | Toiba Tahir "Early Prediction of Sepsis Using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-10 | Issue-4, August 2026, pp.43-47, URL: https://www.ijtsrd.com/papers/ijtsrd141903.pdf

Sepsis is a deadly condition where human body’s reaction to an infection causes organ damage and inflammation throughout the body. Quick detection of sepsis is vital because early detection of sepsis helps reduce mortality risk and makes treatment easier. Existing machine learning solutions for sepsis prediction are designed mostly for ICUs and employ non-disease-specific prediction models. In this work, we present a Machine Learning framework for early prediction of sepsis for non-ICU patients. Individual Random Forests are trained for each of the four infection types – Pneumonia, Urinary Tract Infection(UTI), Intra-Abdominal Infection, and Skin and Soft Tissue Infection(SSTI). Prediction is done based on structured information obtained from patients’ symptoms, vital sign measurements, and lab test results. Trained models are deployed as a Flask based web application which facilitates features like authentication of users, selection of disease type, prediction generation, confidence scoring, risk- stratification and history management of predictions. Our system serves as an example for the potential application of ML driven systems for clinical decision support and demonstrates benefits of disease-specific prediction approaches for early detection of sepsis.

Prediction of Sepsis, Machine Learning, Random Forest, Healthcare Analytics, Flask, Disease- specific Prediction, Clinical Decision Support.


IJTSRD141903
Volume-10 | Issue-4, August 2026
43-47
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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