Year: 2025
Type: article
Abstract: Patients' mortality depends on how effectively hospital treats the patients.ICU is the medical department to handle emergency patients.In real time it is very important to identify high risk patients in ICU department.Machine learning can be applied based on the patient's condition to quickly determine if the patient requires urgent medical intervention from the clinicians or not.Basic vital signs like Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Respiratory Rate (RR), Oxygen saturation (SPO2), Random Blood Sugar (RBS), Temperature, and Pulse Rate (PR) are used as the input for the patients' risk level identification.Highrisk or non-risk categories are the outcome for patient classification.ML algorithms such as Gaussian NB, KNN or DT are applied for the data analysis and for the classification.We'll use a many of supervised learning methods before deciding which one is best for the model.Existing systems rely on classical learning models, which are inefficient and imprecise.They aren't as accurate as the proposed model and take a little longer to process.Many existing topics on patient's classification where they have built models and shown results generated using R language, Python language and data science tools.All existing works are just models, cannot be applied as application useful in real time.In our project work we build an application with ML models that can classify high risk patients and non-risks patients in an emergency department and provides doctors with the information of how to handle patients and treat better.Our proposed work is a real world medical system useful for hospitals and doctors and built using Microsoft tools such as Visual Studio tool and MS SQL Server.
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4413433627