[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121174-en":3,"doc-seo-121174-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121174,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","JURNAL RESTI - Rekayasa Sistem dan Teknologi Informasi - Vol. 9 No. 2 (2025) 185-194","Every patient arriving at the Emergency Department requires rapid assessment to decide inpatient versus outpatient care. Current practice often depends on physician diagnosis, which increases waiting time when patient volume is high. This study develops machine learning and neural network models using publicly available EHR data (3,309 records) to predict patient admission. Models are trained with SVM, Decision Tree, KNN, AdaBoost, and MLPClassifier, then evaluated via confusion matrix, accuracy, precision, recall, and F1-Score.","Accredited SINTA 2 Ranking  \nDecree of the Director General of Higher Education, Research, and Technology, No. 158/E/KPT/2021 Validity period from Volume 5 Number 2 of 2021 to Volume 10 Number 1 of 2026  \nPublished online at: [http://jurnal.iaii.or.id](http://jurnal.iaii.or.id)  \nJURNAL RESTI  \n(Rekayasa Sistem dan Teknologi Informasi)  \nVol. 9 No. 2 (2025) 185-194 e-ISSN: 2580-0760  \nComparative Analysis of Machine Learning Algorithms for Predicting Patient Admission in Emergency Departments Using EHR Data  \nAhmad Abdul Chamid 1*, Ratih Nindyasari2, Muhammad Imam Ghozali3  \n1,2,3Department of Informatics Engineering, Faculty of Engineering, Universitas Muria Kudus, Kudus, Indonesia  \n[1](1 abdul.chamid@umk.ac.id)[ abdul.chamid@umk.ac.id](1 abdul.chamid@umk.ac.id), [2](2 ratih.nindyasari@umk.ac.id)[ ratih.nindyasari@umk.ac.id](2 ratih.nindyasari@umk.ac.id), [3](3imam.ghozali@umk.ac.id)[imam.ghozali@umk.ac.id](3imam.ghozali@umk.ac.id)  \nAbstract  \nEvery patient who is rushed to the Emergency Department needs fast treatment to determine whether the patient should beinpatient or outpatient. However, the existing fact is that deciding whether an inpatient or outpatient must 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 evaluatedfor 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 MLPClassifier and Neural Network models, were proven to be able to outperform other models.  \nKeywords: Emergency Departments; Electronic Health Record; Machine Learning; Neural Networks; Patient Care  \nHow to Cite: A. A. Chamid, R. Nindyasari, and M. I. Ghozali,“Comparative Analysis of Machine Learning Algorithms for Predicting Patient Admission in Emergency Departments Using EHR Data”, J. RESTI (Rekayasa Sist. Teknol. Inf.) , vol. 9, no.  \n2, pp. 185-194, Mar. 2025.  \nDOI: [https://doi.org/10.29207/resti.v9i2.6188](https://doi.org/10.29207/resti.v9i2.6188)  \n1. Introduction  \nThe large number of patients entering the Emergency Department (ED) will result in a crowded room and become a challenge in its management. The problems that arise are limited space, lack of staff, and potential loss of revenue. Limited space when there are many patients in the ED hinders patient flow and increases waiting time. So, it will create a crowded waiting room and increase frustration for patients and staff. Generally, to overcome these obstacles, it is often necessary to utilize non-traditional areas such as hallways to accommodate patients. This emergency solution will hurt patient satisfaction, lower patient experience scores, and create a sense of disorganization for visitors [1],[2] .  \nEvery person rushed to the hospital needs a quick diagnosis to determine whether the patient needs to bean inpatient or outpatient. Generally, to decide whether a patient is an inpatient or outpatient, the doctor on duty  \nmust wait for the decision based on observations made on patients who enter the Emergency Department (ED) . The medical field can utilize technology development by electronically recording health r","cbCaiqxkkglFznzC","https://ap.wps.com/l/cbCaiqxkkglFznzC","pdf",563538,1,10,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is predicting inpatient vs outpatient care important in Emergency Departments?\",\"answer\":\"Rapid decisions are required so patients receive timely treatment. Delays occur when clinicians must wait for diagnosis, especially under high patient volume.\"},{\"question\":\"What data and models are used for the prediction task?\",\"answer\":\"The study uses publicly available EHR data with 3,309 records and builds models including SVM, Decision Tree, KNN, AdaBoost, and neural networks such as MLPClassifier.\"},{\"question\":\"How are the models evaluated and which performs best?\",\"answer\":\"Evaluation uses a confusion matrix and metrics including accuracy, precision, recall, and F1-Score. The best results come from MLPClassifier (accuracy 0.736, F1-Score 0.635), with the neural network achieving accuracy 0.724 and F1-Score 0.640.\"}]","JURNAL RESTI - Rekayasa Sistem dan Teknologi Informasi - Vol. 9 No. 2 (2025) 185-194 | PDF",1785734211,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"jurnal-resti-systems-engineering-and-information-technology-vol-9-no-2-2025-185-194","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/jurnal-resti-systems-engineering-and-information-technology-vol-9-no-2-2025-185-194/121174/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting inpatient vs outpatient care important in Emergency Departments?","Question",{"text":75,"@type":76},"Rapid decisions are required so patients receive timely treatment. Delays occur when clinicians must wait for diagnosis, especially under high patient volume.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and models are used for the prediction task?",{"text":80,"@type":76},"The study uses publicly available EHR data with 3,309 records and builds models including SVM, Decision Tree, KNN, AdaBoost, and neural networks such as MLPClassifier.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated and which performs best?",{"text":84,"@type":76},"Evaluation uses a confusion matrix and metrics including accuracy, precision, recall, and F1-Score. The best results come from MLPClassifier (accuracy 0.736, F1-Score 0.635), with the neural network achieving accuracy 0.724 and F1-Score 0.640.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]