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

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 languageEnglish
Pages(in press)
Number of pages6
StateAccepted/In press - 14 Mar 2017
EventIEEE International Conference on Fuzzy Systems - Naples, Italy

Conference

ConferenceIEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE 2017
CountryItaly
CityNaples
Period9/07/1712/07/17
Internet address

Fingerprint

Soft computing
Fuzzy sets
Decision support systems
Decision making

Keywords

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

Cite this

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 press). Paper presented at IEEE International Conference on Fuzzy Systems, Naples, Italy.

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. (in press) Paper presented at IEEE International Conference on Fuzzy Systems, Naples, Italy.

Research output: Contribution to conferencePaper

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' Paper presented at IEEE International Conference on Fuzzy Systems, Naples, Italy, 9/07/17 - 12/07/17, pp. (in press).
Al Farsi A, Doctor F, Petrovic D, Chandran S, Karyotis C. Interval Valued Data Enhanced Fuzzy Cognitive Map: Towards an Approach for Autism Deduction in Toddlers. 2017. Paper presented at IEEE International Conference on Fuzzy Systems, Naples, Italy.

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

2017. (in press) Paper presented at IEEE International Conference on Fuzzy Systems, Naples, Italy.

Research output: Contribution to conferencePaper

@misc{718df4cf084646dda84ea994de31d174,
title = "Interval Valued Data Enhanced Fuzzy Cognitive Map: Towards an Approach for Autism Deduction in Toddlers",
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.",
keywords = "autism, interval computation, MCHAT, medical decision support system, type-2 fuzzy set, fuzzy cognitive map",
author = "{Al Farsi}, Alya and Faiyaz Doctor and Dobrila Petrovic and Sudhagar Chandran and Charalampos Karyotis",
year = "2017",
month = "3",
pages = "(in press)",

}

TY - CONF

T1 - Interval Valued Data Enhanced Fuzzy Cognitive Map: Towards an Approach for Autism Deduction in Toddlers

AU - Al Farsi,Alya

AU - Doctor,Faiyaz

AU - Petrovic,Dobrila

AU - Chandran,Sudhagar

AU - Karyotis,Charalampos

PY - 2017/3/14

Y1 - 2017/3/14

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.

KW - autism

KW - interval computation

KW - MCHAT

KW - medical decision support system

KW - type-2 fuzzy set

KW - fuzzy cognitive map

M3 - Paper

SP - (in press)

ER -