An Novel Cutting Edge ANN Machine Learning Algorithm for Sepsis Early Prediction and Diagnosis
- Title
- An Novel Cutting Edge ANN Machine Learning Algorithm for Sepsis Early Prediction and Diagnosis
- Creator
- Kanna R.R.; Priya T.M.; Immanuel V.A.; Kirubanand V.B.; Senthilnathan T.; Rohini V.
- Description
- Early detection and diagnosis of sepsis can significantly improve patient outcomes, but current diagnostic methods are limited. The problem addressed in this paper is the early detection and diagnosis of sepsis using machine learning algorithms. Sepsis is a life-threatening condition that can rapidly progress and cause organ failure, leading to increased mortality rates. Early detection and treatment of sepsis are critical for improving patient outcomes and reducing healthcare costs. However, sepsis can be challenging to diagnose, and existing methods have limitations in terms of accuracy and timeliness This research proposes a new cutting-edge Optimized Artificial Neural Network machine learning algorithm for sepsis early prediction and diagnosis. The proposed algorithm combines different data sources, including patient vital signs, laboratory results, and clinical notes, to predict the likelihood of sepsis development. The algorithm was evaluated on a large dataset of patient records and achieved promising results in terms of accuracy, Precision and Recall. The proposed algorithm can potentially serve as a valuable tool for clinicians in the early detection and diagnosis of sepsis, leading to better patient outcomes. 2023 American Institute of Physics Inc.. All rights reserved.
- Source
- AIP Conference Proceedings, Vol-2909, No. 1
- Date
- 2023-01-01
- Publisher
- American Institute of Physics Inc.
- Subject
- Accuracy; ANN; Classification; Machine Learning; Neural Networks
- Coverage
- Kanna R.R., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Priya T.M., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Immanuel V.A., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Kirubanand V.B., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Senthilnathan T., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Rohini V., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 0094243X; ISBN: 978-073544750-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kanna R.R.; Priya T.M.; Immanuel V.A.; Kirubanand V.B.; Senthilnathan T.; Rohini V., “An Novel Cutting Edge ANN Machine Learning Algorithm for Sepsis Early Prediction and Diagnosis,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 25, 2025, https://archives.christuniversity.in/items/show/19571.