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

Dhifaf Aziz, M. A Mohd Ali, Gan Kok Beng, Ismail Mohd. Saiboon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings
Pages170-174
Number of pages5
Volume1
DOIs
Publication statusPublished - 2012
Event2012 4th International Conference on Intelligent and Advanced Systems, ICIAS 2012 - Kuala Lumpur
Duration: 12 Jun 201214 Jun 2012

Other

Other2012 4th International Conference on Intelligent and Advanced Systems, ICIAS 2012
CityKuala Lumpur
Period12/6/1214/6/12

Fingerprint

Fuzzy clustering
Fuzzy inference
Processing

Keywords

  • Adaptive neuro-fuzzy inference system
  • Fuzzy C-means clustering and Fuzzy Subtractive clustering

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Aziz, D., Ali, M. A. M., Kok Beng, G., & Mohd. Saiboon, I. (2012). Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category. In ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings (Vol. 1, pp. 170-174). [6306181] https://doi.org/10.1109/ICIAS.2012.6306181

Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category. / Aziz, Dhifaf; Ali, M. A Mohd; Kok Beng, Gan; Mohd. Saiboon, Ismail.

ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings. Vol. 1 2012. p. 170-174 6306181.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aziz, D, Ali, MAM, Kok Beng, G & Mohd. Saiboon, I 2012, Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category. in ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings. vol. 1, 6306181, pp. 170-174, 2012 4th International Conference on Intelligent and Advanced Systems, ICIAS 2012, Kuala Lumpur, 12/6/12. https://doi.org/10.1109/ICIAS.2012.6306181
Aziz D, Ali MAM, Kok Beng G, Mohd. Saiboon I. Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category. In ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings. Vol. 1. 2012. p. 170-174. 6306181 https://doi.org/10.1109/ICIAS.2012.6306181
Aziz, Dhifaf ; Ali, M. A Mohd ; Kok Beng, Gan ; Mohd. Saiboon, Ismail. / Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category. ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings. Vol. 1 2012. pp. 170-174
@inproceedings{452a3494fee54f4c94bac8cdf8f8e250,
title = "Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category",
abstract = "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.",
keywords = "Adaptive neuro-fuzzy inference system, Fuzzy C-means clustering and Fuzzy Subtractive clustering",
author = "Dhifaf Aziz and Ali, {M. A Mohd} and {Kok Beng}, Gan and {Mohd. Saiboon}, Ismail",
year = "2012",
doi = "10.1109/ICIAS.2012.6306181",
language = "English",
isbn = "9781457719677",
volume = "1",
pages = "170--174",
booktitle = "ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings",

}

TY - GEN

T1 - Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category

AU - Aziz, Dhifaf

AU - Ali, M. A Mohd

AU - Kok Beng, Gan

AU - Mohd. Saiboon, Ismail

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Adaptive neuro-fuzzy inference system

KW - Fuzzy C-means clustering and Fuzzy Subtractive clustering

UR - http://www.scopus.com/inward/record.url?scp=84867939669&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867939669&partnerID=8YFLogxK

U2 - 10.1109/ICIAS.2012.6306181

DO - 10.1109/ICIAS.2012.6306181

M3 - Conference contribution

AN - SCOPUS:84867939669

SN - 9781457719677

VL - 1

SP - 170

EP - 174

BT - ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings

ER -