Using machine learning techniques for predictionemergency department visits
| Autor: | Jin-Rong Guo, 郭進榮 |
|---|---|
| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 In the emergency room of the hospital, patients who need emergency medical care are waiting for emergency treatment, so the emergency room time every minute is very valuable. Because the health insurance system is very serious and the emergency medical resources are limited, if we can predict how many patients will appear in the emergency room, the hospital will be able to dispatch the resources more effectively, but also reduce the pressure of the medical staff in the emergency, improve the medical Quality, emergency patients waiting time will be reduced, so the emergency medical staff how to effectively allocate resources to provide patients with the best quality service is a question worthy of discussion. This study uses ten prediction modes such as Simple Linear Regression, Linear Regression, Multilayer Perceptron, SVR, RBF Network, IBK, K Star, LWL, REP Tree and M5 Rules in Weka system. The purpose of this paper is to compare the prediction techniques of machine learning Method to find out the forecasting technology suitable for predicting the number of emergencies, hoping to be the basis for the allocation of human resources and medical resources. The experimental results show that the SVR and REP Tree prediction models are more suitable for the prediction of the number of emergencies provided by the regional hospitals. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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