The nature of statistical learning theory
The nature of statistical learning theory
Are Electronic Emergency Department Data Predictive of Heat-Related Mortality?
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Predicting box-office success of motion pictures with neural networks
Expert Systems with Applications: An International Journal
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
Applying HFMEA to Prevent Chemotherapy Errors
Journal of Medical Systems
Journal of Medical Systems
Hi-index | 0.00 |
The purpose of triage is to prevent the delay of treatment for patients in real emergencies due to excessive numbers of patients in the hospital. This study uses the data of patients of consistent triage to develop the triage prediction model. By integrating Principal Component Analysis (PCA) and Support Vector Machine (SVM), the anomaly detection (overestimate and underestimate) prediction accuracy rate can be 100 %, which is better than the accuracy rate of SVM (about 89.2 %) or Back- propagation Neural Networks (BPNN) (96.71 %); afterwards, this study uses Support Vector Regression (SVR) to adopt Genetic Algorithm (GA) to determine three SVR parameters to predict triage. After using the scroll data predictive values, we calculate the Absolute Percentage Error (APE) of each scroll data. The resulting SVR's Mean Absolute Percentage Error (MAPE) is 3.78 %, and BPNN's MAPE is 5.99 %; therefore, the proposed triage prediction model of this study can effectively predict anomaly detection and triage.