Paper Key : IRJ************455
Author: Prathibha B S ,Bharat P,Dayanand S,Girija Patil,Manish M P
Date Published: 01 Jul 2024
Abstract
Sepsis remains a critical challenge in healthcare, necessitating timely detection and intervention to mitigate itslifethreatening consequences. This project aims to revolutionizesepsis detection by use of real-time machine learning algorithms. Through the utilization of sophisticated techniques includingRandom Forest, Naive Bayes, and Decision Trees, the study aimsto develop a dynamic and accurate system for early sepsis pre- diction. By analyzing diverse patient data sources encompassingvital signs and clinical history, the project prioritizes the earlyidentification of sepsis, aiming to facilitate prompt interventions and improve patient outcomes. The research aligns with the Third International Consensus onsepsis-3 criteria, emphasizing the importance of evolving diagnostic accuracy. By leveraging comprehensive datasets and advancedalgorithms, the project seeks to overcome the limitations of existing sepsis detection methodologies, such as limited sensitivityand delayed interventions. The systems objectives encompass thecreation of a user-friendly interface for healthcare professionals, enabling seamless integration into existing clinical workflows. Furthermore, the projects impact extends beyond immediatecost-effectiveness, aiming to enhance overall healthcare efficiencyand patient care. Through optimized resource allocation andimproved diagnostic speed, the system aims to streamline health- care processes and contribute to long-term cost savings. This research holds promise for transforming healthcare practices, improving patient outcomes, and establishing a robust frameworkfor efficient sepsis detection in clinical settings.