Work-in-progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system

Sheng Ta Hsieh; Chun Ling Lin

doi:10.4108/icst.iniscom.2015.259009

Work-in-progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system

Sheng Ta Hsieh
, Chun Ling Lin

Department of Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This study combines adaptive neuro-fuzzy inference system (ANFIS) with greedy forward feature selection to develop the intelligent diagnosis system. Two different membership functions (MFs), Trapezoidal and Gaussian, are adopted during the training process of ANFIS in order to compare the diagnosis accuracy of Trapezoidal MF with one of Gaussian MF. The comparison of ANFIS values with simulated data indices that adoption of both Trapezoidal and Gaussian MF in proposed system achieve satisfactory accuracy (>96%). Furthermore, the accuracy of ANFIS with Gaussian MF is above 98%. Hence, the intelligent diagnosis system can provide a preliminary result to physicians so that the doctor could quickly and accurately decide whether patient have cold or influenza.

Original language	English
Title of host publication	Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	177-180
Number of pages	4
ISBN (Electronic)	9781631900228
DOIs	https://doi.org/10.4108/icst.iniscom.2015.259009
State	Published - 14 Jul 2015
Event	1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015 - Tokyo, Japan Duration: 2 Mar 2015 → 4 Mar 2015

Publication series

Name	Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015

Conference

Conference	1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015
Country/Territory	Japan
City	Tokyo
Period	2/03/15 → 4/03/15

Bibliographical note

Publisher Copyright:
© 2015 ICST.

Keywords

Adaptive neuro-fuzzy inference system (ANFIS)
Greedy forward feature selection
Intelligent diagnosis system
Membership function (MF)

Access to Document

10.4108/icst.iniscom.2015.259009

Cite this

Hsieh, S. T., & Lin, C. L. (2015). Work-in-progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system. In Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015 (pp. 177-180). Article 7157842 (Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.4108/icst.iniscom.2015.259009

Hsieh, Sheng Ta ; Lin, Chun Ling. / Work-in-progress : An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system. Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 177-180 (Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015).

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abstract = "This study combines adaptive neuro-fuzzy inference system (ANFIS) with greedy forward feature selection to develop the intelligent diagnosis system. Two different membership functions (MFs), Trapezoidal and Gaussian, are adopted during the training process of ANFIS in order to compare the diagnosis accuracy of Trapezoidal MF with one of Gaussian MF. The comparison of ANFIS values with simulated data indices that adoption of both Trapezoidal and Gaussian MF in proposed system achieve satisfactory accuracy (>96\%). Furthermore, the accuracy of ANFIS with Gaussian MF is above 98\%. Hence, the intelligent diagnosis system can provide a preliminary result to physicians so that the doctor could quickly and accurately decide whether patient have cold or influenza.",

keywords = "Adaptive neuro-fuzzy inference system (ANFIS), Greedy forward feature selection, Intelligent diagnosis system, Membership function (MF)",

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Hsieh, ST & Lin, CL 2015, Work-in-progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system. in Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015., 7157842, Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015, Institute of Electrical and Electronics Engineers Inc., pp. 177-180, 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015, Tokyo, Japan, 2/03/15. https://doi.org/10.4108/icst.iniscom.2015.259009

Work-in-progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system. / Hsieh, Sheng Ta; Lin, Chun Ling.
Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 177-180 7157842 (Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T2 - 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015

AU - Hsieh, Sheng Ta

AU - Lin, Chun Ling

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N2 - This study combines adaptive neuro-fuzzy inference system (ANFIS) with greedy forward feature selection to develop the intelligent diagnosis system. Two different membership functions (MFs), Trapezoidal and Gaussian, are adopted during the training process of ANFIS in order to compare the diagnosis accuracy of Trapezoidal MF with one of Gaussian MF. The comparison of ANFIS values with simulated data indices that adoption of both Trapezoidal and Gaussian MF in proposed system achieve satisfactory accuracy (>96%). Furthermore, the accuracy of ANFIS with Gaussian MF is above 98%. Hence, the intelligent diagnosis system can provide a preliminary result to physicians so that the doctor could quickly and accurately decide whether patient have cold or influenza.

AB - This study combines adaptive neuro-fuzzy inference system (ANFIS) with greedy forward feature selection to develop the intelligent diagnosis system. Two different membership functions (MFs), Trapezoidal and Gaussian, are adopted during the training process of ANFIS in order to compare the diagnosis accuracy of Trapezoidal MF with one of Gaussian MF. The comparison of ANFIS values with simulated data indices that adoption of both Trapezoidal and Gaussian MF in proposed system achieve satisfactory accuracy (>96%). Furthermore, the accuracy of ANFIS with Gaussian MF is above 98%. Hence, the intelligent diagnosis system can provide a preliminary result to physicians so that the doctor could quickly and accurately decide whether patient have cold or influenza.

KW - Adaptive neuro-fuzzy inference system (ANFIS)

KW - Greedy forward feature selection

KW - Intelligent diagnosis system

KW - Membership function (MF)

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Hsieh ST, Lin CL. Work-in-progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system. In Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 177-180. 7157842. (Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015). doi: 10.4108/icst.iniscom.2015.259009

Work-in-progress: An intelligent diagnosis influenza system based on adaptive neuro-fuzzy inference system

Abstract

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Keywords

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