Authors: B. M. Promod Kumar, Namith Kumar Y, Pruthvi M C, Poorvik K V, Jagadeesh M
Abstract: This concept is based on patient’s classification in an Emergency Department in a hospital according to their critical conditions. 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 [1]. 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 [2]. High-risk 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 [5]. Our proposed work is a real-world medical system useful for hospitals and doctors and built using trending tools such as Visual Studio code, PYTHON and MYSQL Server.
