A hybrid intelligent system for skin disease diagnosis

Nisreen I.Abo Dabowsa; Nasser M. Amaitik; Abdelsalam M. Maatuk; Shadi A. Aljawarneh

doi:10.1109/ICEngTechnol.2017.8308157

A hybrid intelligent system for skin disease diagnosis

Nisreen I.Abo Dabowsa, Nasser M. Amaitik, Abdelsalam M. Maatuk, Shadi A. Aljawarneh

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

In this paper, an intelligent decision support system has been proposed for skin disease diagnosis using a hybrid model of Case-Based Reasoning and Artificial Neural Network techniques. The proposed model uses nine input variables (attributes) that have a major effect on the skin diagnosing process. The output of the model is the diagnosis and the treatment. An interactive and user friendly computer application has been developed in order to realize the approach. We have applied the system on a real-world data collected from a dermatology department. The model has been validated and the system tested using a separate set of data (test cases). The results demonstrate that the proposed intelligent system is feasible, and its performance is good and acceptable.

Original language	English
Title of host publication	Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538619490
DOIs	https://doi.org/10.1109/ICEngTechnol.2017.8308157
Publication status	Published - 7 Mar 2018
Event	2017 International Conference on Engineering and Technology, ICET 2017 - Antalya, Turkey Duration: 21 Aug 2017 → 23 Aug 2017

Publication series

Name	Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017
Volume	2018-January

Conference

Conference	2017 International Conference on Engineering and Technology, ICET 2017
Country/Territory	Turkey
City	Antalya
Period	21/08/17 → 23/08/17

Keywords

ANN
CBR
diseases diagnosis
intelligent system

Access to Document

10.1109/ICEngTechnol.2017.8308157

Cite this

Dabowsa, N. I. A., Amaitik, N. M., Maatuk, A. M., & Aljawarneh, S. A. (2018). A hybrid intelligent system for skin disease diagnosis. In Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017 (pp. 1-6). (Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017; Vol. 2018-January). IEEE. https://doi.org/10.1109/ICEngTechnol.2017.8308157

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title = "A hybrid intelligent system for skin disease diagnosis",

abstract = "In this paper, an intelligent decision support system has been proposed for skin disease diagnosis using a hybrid model of Case-Based Reasoning and Artificial Neural Network techniques. The proposed model uses nine input variables (attributes) that have a major effect on the skin diagnosing process. The output of the model is the diagnosis and the treatment. An interactive and user friendly computer application has been developed in order to realize the approach. We have applied the system on a real-world data collected from a dermatology department. The model has been validated and the system tested using a separate set of data (test cases). The results demonstrate that the proposed intelligent system is feasible, and its performance is good and acceptable.",

keywords = "ANN, CBR, diseases diagnosis, intelligent system",

author = "Dabowsa, {Nisreen I.Abo} and Amaitik, {Nasser M.} and Maatuk, {Abdelsalam M.} and Aljawarneh, {Shadi A.}",

year = "2018",

month = mar,

day = "7",

doi = "10.1109/ICEngTechnol.2017.8308157",

language = "English",

series = "Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017",

publisher = "IEEE",

pages = "1--6",

booktitle = "Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017",

address = "United States",

note = "2017 International Conference on Engineering and Technology, ICET 2017 ; Conference date: 21-08-2017 Through 23-08-2017",

}

Dabowsa, NIA, Amaitik, NM, Maatuk, AM & Aljawarneh, SA 2018, A hybrid intelligent system for skin disease diagnosis. in Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017. Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, vol. 2018-January, IEEE, pp. 1-6, 2017 International Conference on Engineering and Technology, ICET 2017, Antalya, Turkey, 21/08/17. https://doi.org/10.1109/ICEngTechnol.2017.8308157

A hybrid intelligent system for skin disease diagnosis. / Dabowsa, Nisreen I.Abo; Amaitik, Nasser M.; Maatuk, Abdelsalam M. et al.
Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017. IEEE, 2018. p. 1-6 (Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017; Vol. 2018-January).

Research output: Chapter in Book/Published conference output › Conference publication

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T1 - A hybrid intelligent system for skin disease diagnosis

AU - Dabowsa, Nisreen I.Abo

AU - Amaitik, Nasser M.

AU - Maatuk, Abdelsalam M.

AU - Aljawarneh, Shadi A.

PY - 2018/3/7

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N2 - In this paper, an intelligent decision support system has been proposed for skin disease diagnosis using a hybrid model of Case-Based Reasoning and Artificial Neural Network techniques. The proposed model uses nine input variables (attributes) that have a major effect on the skin diagnosing process. The output of the model is the diagnosis and the treatment. An interactive and user friendly computer application has been developed in order to realize the approach. We have applied the system on a real-world data collected from a dermatology department. The model has been validated and the system tested using a separate set of data (test cases). The results demonstrate that the proposed intelligent system is feasible, and its performance is good and acceptable.

AB - In this paper, an intelligent decision support system has been proposed for skin disease diagnosis using a hybrid model of Case-Based Reasoning and Artificial Neural Network techniques. The proposed model uses nine input variables (attributes) that have a major effect on the skin diagnosing process. The output of the model is the diagnosis and the treatment. An interactive and user friendly computer application has been developed in order to realize the approach. We have applied the system on a real-world data collected from a dermatology department. The model has been validated and the system tested using a separate set of data (test cases). The results demonstrate that the proposed intelligent system is feasible, and its performance is good and acceptable.

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A hybrid intelligent system for skin disease diagnosis

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