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Assessing the Value of Machine Learning for Proactive Hospitalization Forecasting

Authors:

K Madhu sudhan reddy ^*1, P Vishnu vardhan ^*2

^*1 Assistant Professor, Department of MCA,

Annamacharya Institute of Technology & Science,Tirupati, Andhra Pradesh, India.

Email: mca.madhureddy@gmail.com

^2*Post Gradute, Department of MCA,

Annamacharya Institute of Technology & Science,Tirupati, Andhra Pradesh, India.

Email: pillimetlavishnuvardhan@gmail.com

ABSTRACT: Proactive hospitalization forecasting, the ability to predict hospital admissions in advance, can significantly improve healthcare efficiency and patient outcomes. Traditionally, hospitals rely on reactive strategies to manage patient admissions, which can lead to overcrowding, resource strain, and delayed medical interventions. Machine learning (ML) offers a promising solution for predicting hospitalization events by analyzing vast datasets from electronic health records (EHRs), historical admissions, patient demographics, and clinical conditions. This paper investigates the potential value of machine learning in forecasting hospitalizations, focusing on various ML algorithms such as logistic regression, decision trees, and neural networks. Through a case study using real-world healthcare data, the performance of these models is evaluated based on accuracy, precision, recall, and F1-score. Results demonstrate that machine learning can significantly enhance forecasting accuracy, allowing healthcare providers to anticipate patient needs, optimize resource allocation, and reduce the likelihood of emergency admissions. Furthermore, the study explores how machine learning-driven forecasts can enable more personalized and efficient healthcare delivery, benefiting both patients and healthcare institutions.

Keywords : EHR,Machine Learning, healthcare data.