International Journal Of Imaging

Abstract

PREDICTING HOSPITAL ADMISSION TRENDS WITH MACHINE LEARNING TECHNIQUES FOR ENHANCED HEALTH CARE MANAGEMENT

Author: Teppala Dinesh, Godugu Naveen Kumar, Allam Akash, Dr. N. Krishnaiah

Abstract:

Hospital Readmissions represent a significant challenge in modern healthcare systems, contributing to elevated costs and potential patient detriment. This project seeks to address this issue by leveraging advanced supervised learning models to predict hospital readmissions based on emergency department data. Historically, predicting readmissions relied on simplistic statistical methods and clinical judgment, which often lacked accuracy due to their inability to handle the complexities of large datasets and multifactorial variables. With the rise of electronic health records (EHRs) and big data analytics, there has been a shift towards utilizing comprehensive patient data for more precise predictions. Traditional systems, however, face limitations such as difficulty in managing intricate data interactions, static models that do not adapt to evolving patient conditions, and suboptimal predictive accuracy due to insufficient feature handling. This project aims to overcome