Abstract
Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good–excellent prognostic efficacy, and it outperformed other traditional models.
Highlights
Acute gastrointestinal bleeding (GIB) is a frequent complication, with 100–200 per 100000 people experiencing it each year in the upper gastrointestinal system and 20.5–27.0 per 100000 people experiencing it in the lower gastrointestinal system
A huge proportion of WCE images acquired from openly accessible WCE movies are employed, and it is discovered that the suggested model outperformed the outcomes achieved by various state-of-the-art techniques
During follow-up of anticoagulated patients, GIB maximized the risk of eventual fatality, emphasizing the significance of prophylaxis
Summary
Acute gastrointestinal bleeding (GIB) is a frequent complication, with 100–200 per 100000 people experiencing it each year in the upper gastrointestinal system and 20.5–27.0 per 100000 people experiencing it in the lower gastrointestinal system. Machine learning algorithms would be used to improve physician risk evaluation and decision-making in the future of gastrointestinal bleeding. Several experts say categorizing people with acute GIB into high- and low-risk categories at the outset, with others suggesting the use of prognostic risk prediction techniques. Several risk evaluation measures were created and tested for their potential to forecast results including death, the requirement for interventions, extrableeding, and/or excessive bleeding. They discover meaningful indicators for a framework using traditional statistical studies, with every indicator being given simple-to-use values by an observer. In this article, we suggest an optimized machine learning framework using spider monkey optimization for the risk assessment of gastrointestinal bleeding. Part V concludes the overall idea of the paper
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
