Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category

This paper describes the fuzzy clustering method to initialize the Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting primary triage category. Fuzzy C-means (FCM) and Fuzzy Subtractive clustering (FSC) are the most commonly used unsupervised clustering methods to initialize the ANFIS model. A total of 135 data was extracted from Objective Primary Triage Scale (OPTS) records obtained from Emergency Department UKMMC. These data was used to develop the ANFIS model and predict the primary triage category. The classification accuracy of the ANFIS model using fuzzy clustering method in predicting the primary triage category is 98.4%. The FCM method produced fewer rules and needed less processing time to reach the RMSE of 0.127 compared to the FSC method. These results suggest that FCM clustering will be used to predict the primary triage category.