Interval Valued Data Enhanced Fuzzy Cognitive Map: Towards an Approach for Autism Deduction in Toddlers

Alya Al Farsi; Faiyaz Doctor; Dobrila Petrovic; Sudhagar Chandran; Charalampos Karyotis

doi:10.1109/FUZZ-IEEE.2017.8015702

Interval Valued Data Enhanced Fuzzy Cognitive Map: Towards an Approach for Autism Deduction in Toddlers

Alya Al Farsi, Faiyaz Doctor, Dobrila Petrovic, Sudhagar Chandran, Charalampos Karyotis

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

8 Citations (Scopus)

47 Downloads (Pure)

Abstract

Fuzzy Cognitive Maps (FCMs) are a soft computing technique characterized by robust properties that make them an effective technique for medical decision support systems. Making decisions within a medical domain is difficult due to the existence of high levels of uncertainty. The sources of this uncertainty can be due to the variation of physicians' opinions and experiences. The structure of existing FCMs is based on type -1 fuzzy sets in order to represent the causal relations among concepts of the modeled system. Therefore, the ability of the FCM to handle high levels of uncertainties and deliver accurate results can be hindered. In this paper, we propose using the Interval Agreement Approach to model the weights of links in FCMs to capture high level uncertainties in the presence of imprecise data acquired from different medical experts to enhance its decision modelling and reasoning capability. The proposed model is used in identifying if a child is diagnosed with an Autism Spectrum Disorder (ASD) where the Modified Checklist for Autism in Toddlers is used as a standard tool to derive the inputs for the FCMs. Initial results demonstrate that the proposed method outperforms conventional FCMs in classifying ASD based on a dataset of diagnosed cases.

Original language	English
Title of host publication	2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Place of Publication	Naples, Italy
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-5090-6034-4
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2017.8015702
Publication status	Published - 2017
Event	IEEE International Conference on Fuzzy Systems - Naples, Italy Duration: 9 Jul 2017 → 12 Jul 2017 https://www.fuzzieee2017.org/

Conference

Conference	IEEE International Conference on Fuzzy Systems
Abbreviated title	FUZZ-IEEE 2017
Country/Territory	Italy
City	Naples
Period	9/07/17 → 12/07/17
Internet address	https://www.fuzzieee2017.org/

Bibliographical note

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

autism
interval computation
MCHAT
medical decision support system
type-2 fuzzy set
fuzzy cognitive map

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10.1109/FUZZ-IEEE.2017.8015702

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Interval Valued Data Enhanced Fuzzy Cognitive Map: Towards an Approach for Autism Deduction in Toddlers. / Al Farsi, Alya; Doctor, Faiyaz; Petrovic, Dobrila; Chandran, Sudhagar; Karyotis, Charalampos.

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Naples, Italy : IEEE, 2017.

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

Al Farsi, A, Doctor, F, Petrovic, D, Chandran, S & Karyotis, C 2017, Interval Valued Data Enhanced Fuzzy Cognitive Map: Towards an Approach for Autism Deduction in Toddlers. in 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, Naples, Italy, IEEE International Conference on Fuzzy Systems, Naples, Italy, 9/07/17. https://doi.org/10.1109/FUZZ-IEEE.2017.8015702

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N2 - Fuzzy Cognitive Maps (FCMs) are a soft computing technique characterized by robust properties that make them an effective technique for medical decision support systems. Making decisions within a medical domain is difficult due to the existence of high levels of uncertainty. The sources of this uncertainty can be due to the variation of physicians' opinions and experiences. The structure of existing FCMs is based on type -1 fuzzy sets in order to represent the causal relations among concepts of the modeled system. Therefore, the ability of the FCM to handle high levels of uncertainties and deliver accurate results can be hindered. In this paper, we propose using the Interval Agreement Approach to model the weights of links in FCMs to capture high level uncertainties in the presence of imprecise data acquired from different medical experts to enhance its decision modelling and reasoning capability. The proposed model is used in identifying if a child is diagnosed with an Autism Spectrum Disorder (ASD) where the Modified Checklist for Autism in Toddlers is used as a standard tool to derive the inputs for the FCMs. Initial results demonstrate that the proposed method outperforms conventional FCMs in classifying ASD based on a dataset of diagnosed cases.

AB - Fuzzy Cognitive Maps (FCMs) are a soft computing technique characterized by robust properties that make them an effective technique for medical decision support systems. Making decisions within a medical domain is difficult due to the existence of high levels of uncertainty. The sources of this uncertainty can be due to the variation of physicians' opinions and experiences. The structure of existing FCMs is based on type -1 fuzzy sets in order to represent the causal relations among concepts of the modeled system. Therefore, the ability of the FCM to handle high levels of uncertainties and deliver accurate results can be hindered. In this paper, we propose using the Interval Agreement Approach to model the weights of links in FCMs to capture high level uncertainties in the presence of imprecise data acquired from different medical experts to enhance its decision modelling and reasoning capability. The proposed model is used in identifying if a child is diagnosed with an Autism Spectrum Disorder (ASD) where the Modified Checklist for Autism in Toddlers is used as a standard tool to derive the inputs for the FCMs. Initial results demonstrate that the proposed method outperforms conventional FCMs in classifying ASD based on a dataset of diagnosed cases.

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ER -

Interval Valued Data Enhanced Fuzzy Cognitive Map: Towards an Approach for Autism Deduction in Toddlers

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Keywords

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