Comparative Analysis of Machine Learning Algorithms for Predicting Patient Admission in Emergency Departments Using EHR Data

Comparative Analysis of Machine Learning Algorithms for Predicting Patient Admission in Emergency Departments Using EHR Data

Emergency Departments; Electronic Health Record; Machine Learning; Neural Networks; Patient Care

very patient who is rushed to the Emergency Department needs fast treatment to determinewhether the patient should be inpatientor outpatient. However, the existing fact is that decidingwhether an inpatientor outpatientmust wait for the diagnosis made by the existing doctor, so if there are many patients, it generally takes quite a long time. So, to predict patient admissions to the emergency unit, a machine learning model that can be fast and accurate is needed.Therefore, this study developed a machine learning and neural network model to determine patient care in Emergency Departments. This study uses publicly available electronic health record (EHR) data, which is 3,309. The model development process uses machine learning methods (SVM, Decision Tree, KNN, AdaBoost, MLPClassifier) and neural networks. The model that has been obtained is then evaluated for its performance using a confusion matrix and several matrices such as accuracy, precision, recall, and F1-Score. The results of the model performance evaluation were compared, and the best model was obtained, namely the MLPClassifier model with an accuracy value = 0.736 and an F1-Score value = 0.635, and the Neural Network model obtained an accuracy value = 0.724 and an F1-Score value = 0.640. The best models obtained in this study, namely the MLPClassifierand Neural Network models, were proven to be able to outperform other models

Ahmad Abdul Chamid1*, Ratih Nindyasari2, Muhammad Imam Ghozali3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6188/1027

Department of Informatics Engineering, Faculty of Engineering, Universitas Muria Kudus,Kudus,Indonesia

07-03-2025

FAJAR BAGUS W

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