Machine Learning Analysis of Patient Age Classification Based on Inpatient Visit Data Using Random Forest and SVM Algorithms

Abstract

The increase in inpatient care visits in hospitals has led to a growing need for data driven analysis to classify patient demographics, particularly age groups. This study applies supervised machine learning algorithms Random Forest and Support Vector Machine (SVM) to classify inpatient visit data based on age categories: children, adults, and elderly. A dataset from hospital information systems, consisting of features such as gender, diagnosis, length of stay, and referral source, was used for model training and evaluation. The system was developed using Python and integrated into a web interface to allow real-time predictions. Evaluation results show that Random Forest slightly outperforms SVM in accuracy and recall. The findings of this study should help hospitals make decisions, especially when it comes to allocating resources and differentiating services according to patient age.