Explainable artificial intelligence multimodal of autism triage levels using fuzzy approach-based multi-criteria decision-making and LIME

Alamoodi, Abdullah Hussein

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Type :	Article
Subject :	T Technology (General)
ISBN :	1562-2479
Main Author :	Alamoodi, Abdullah Hussein
Title :	Explainable artificial intelligence multimodal of autism triage levels using fuzzy approach-based multi-criteria decision-making and LIME
Hits :	103

Place of Production :	Tanjung Malim
Publisher :	Fakulti Komputeran & Meta-Teknologi
Year of Publication :	2024
Notes :	International Journal of Fuzzy Systems
Corporate Name :	Universiti Pendidikan Sultan Idris
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Abstract : Universiti Pendidikan Sultan Idris

A new framework for explainable artificial intelligence in the context of multimodal triage for autism spectrum disorders (ASD) using a fuzzy approach-based multi-criteria decision-making (MCDM) is proposed in this study. The framework consists of five phases. In the first phase, a real ASD dataset of 538 autistic patients is obtained and diagnosed based on 42 medical and sociodemographic criteria. In the second phase, an ASD methodology for triaging the 538 autistic patients into three levels (i.e., minor, moderate, and urgent) is presented using fuzzy approach-based MCDM techniques, namely, fuzzy Delphi method and fuzzy-weighted zero-inconsistency, followed by the processes for triaging autism patients. In the third phase, cost-sensitive learning is employed to balance two ASD datasets: one labeled based on the ASD triage methodology and the other labeled by specialized psychologists. Two multimodal of artificial intelligence are developed in the fourth phase using nine machine-learning algorithms for the balanced ASD datasets. The evaluation of the two multimodal setups is carried out using nine metrics. In the fifth phase, the local interpretable model-agnostic explanations (LIME) model is used to interpret the models using two scenarios. Four new algorithms are presented in the developed framework. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2023.

References

Nadel, J., Grynszpan, O., Martin, J.-C.: Autism and socially interactive agents. In: The Handbook on Socially Interactive Agents, pp. 437–462 (2022). https://doi.org/10.1145/3563659.3563673.

Zager, D., Cihak, D.F., Stone-MacDonald, A.: Autism Spectrum Disorders. Routledge, London (2016). https://doi.org/10.4324/9781315794181.

Fernandez-Prieto, M., et al.: Executive functioning: a mediator between sensory processing and behaviour in autism spectrum disorder. J. Autism Dev. Disord. 51(6), 2091–2103 (2021). https://doi.org/10.1007/s10803-020-04648-4.

Lai, A.G., Chang, W.H., Skuse, D.: Autism and mental illness in children and young people require standardised approaches for assessment and treatment. Lancet Reg. Health Eur. (2022). https://doi.org/10.1016/j.lanepe.2022.100360.

Ghahari, N., Yousefian, F., Behzadi, S., Jalilzadeh, A.: Rural–urban differences in age at autism diagnosis: a multiple model analysis. Iran. J. Psychiatry 17(3), 294–303 (2022). https://doi.org/10.18502/ijps.v17i3.9729.

Fernell, E., Eriksson, M.A., Gillberg, C.: Early diagnosis of autism and impact on prognosis: a narrative review. Clin. Epidemiol. 5(1), 33–43 (2013). https://doi.org/10.2147/CLEP.S41714.

Green, J., Leadbitter, K., Ainsworth, J., Bucci, S.: An integrated early care pathway for autism. Lancet Child Adolesc. Health 6(5), 335–344 (2022). https://doi.org/10.1016/S2352-4642(22)00037-2.

Joudar, S.S., Albahri, A.S., Hamid, R.A.: Intelligent triage method for early diagnosis autism spectrum disorder (ASD) based on integrated fuzzy multi-criteria decision-making methods. Inform. Med. Unlocked 36, 101131 (2023). https://doi.org/10.1016/j.imu.2022.101131.

Ferguson, E.F., Jimenez-Mun˜oz, M., Feerst, H., Vernon, T.W.: Predictors of satisfaction with autism treatment services during COVID-19. J. Autism Dev. Disord. 52(8), 3686–3697 (2022). https://doi.org/10.1007/s10803-021-05232-0.

Alqaysi, M.E., Albahri, A.S., Hamid, R.A.: Diagnosis-based hybridization of multimedical tests and sociodemographic characteristics of autism spectrum disorder using artificial intelligence and machine learning techniques: a systematic review. Int. J. Telemed. Appl. (2022). https://doi.org/10.1155/2022/3551528.

Joudar, S.S., Albahri, A.S., Hamid, R.A.: Triage and prioritybased healthcare diagnosis using artificial intelligence for autism spectrum disorder and gene contribution: a systematic review. Comput. Biol. Med. 146, 105553 (2022). https://doi.org/10.1016/j.compbiomed.2022.105553.

Alqaysi, M.E., Albahri, A.S., Hamid, R.A.: Hybrid diagnosis models for autism patients based on medical and sociodemographic features using machine learning and multicriteria decision-making (MCDM) techniques: an evaluation and benchmarking framework. Comput. Math. Methods Med. 2022, 9410222 (2022). https://doi.org/10.1155/2022/9410222.

Albahri, A.S., et al.: Towards physician’s experience: development of machine learning model for the diagnosis of autism spectrum disorders based on complex T-spherical fuzzy-weighted zero-inconsistency method. Comput. Intell. (2022). https://doi.org/10.1111/coin.12562.

Koyamparambath, A., Adibi, N., Szablewski, C., Adibi, S.A., Sonnemann, G.: Implementing artificial intelligence techniques to predict environmental impacts: case of construction products. Sustainability 14(6), 3699 (2022). https://doi.org/10.3390/su14063699.

Barua, P.D., et al.: Artificial intelligence enabled personalised assistive tools to enhance education of children with neurodevelopmental disorders—a review. Int. J. Environ. Res. Public Health 19(3), 1192 (2022). https://doi.org/10.3390/ijerph19031192.

Barredo Arrieta, A., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020). https://doi.org/10.1016/j.inffus.2019.12.012.

Pawar, U., O’Shea, D., Rea, S., O’Reilly, R.: Explainable AI in healthcare. In: 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, Cyber SA 2020, pp. 1–2 (2020). https://doi.org/10.1109/CyberSA49311.2020.9139655.

Ribeiro, M.T., Singh, S., Guestrin, C.: ‘Why should I trust you?’ Explaining the predictions of any classifier. In: NAACL-HLT 2016—2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session, pp. 97–101 (2016). https://doi.org/10.18653/v1/n16-3020.

Quinn, T.P., Jacobs, S., Senadeera, M., Le, V., Coghlan, S.: The three ghosts of medical AI: can the black-box present deliver? Artif. Intell. Med. 124, 102158 (2022). https://doi.org/10.1016/j.artmed.2021.102158.

Payrovnaziri, S.N., et al.: Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review. J. Am. Med. Inform. Assoc. 27(7), 1173–1185 (2020). https://doi.org/10.1093/jamia/ocaa053.

Abdo, H., Flaus, J.M.: Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory. Int. J. Prod. Res. 54(19), 5862–5885 (2016). https://doi.org/10.1080/00207543.2016.1184348.

Liao, H., Yang, S., Kazimieras Zavadskas, E., Sˇkare, M.: An overview of fuzzy multi-criteria decision-making methods in hospitality and tourism industries: bibliometrics, methodologies, applications and future directions. Econ. Res. Istraz. (2022). https://doi.org/10.1080/1331677X.2022.2150871.

Sharma, M., Sharma, P., Pachori, R.B., Acharya, U.R.: Dual-tree complex wavelet transform-based features for automated alcoholism identification. Int. J. Fuzzy Syst. 20(4), 1297–1308 (2018). https://doi.org/10.1007/s40815-018-0455-x.

Alsalem, M.A., Mohammed, R., Albahri, O.S., Zaidan, A.A., Alamoodi, A.H., Dawood, K., et al.: Rise of multiattribute decision-making in combating COVID-19: a systematic review of the state-of-the-art literature. Int. J. Intell. Syst. 37(6), 3514–3624 (2022).

Alsalem, M.A., Alamoodi, A.H., Albahri, O.S., Dawood, K.A., Mohammed, R.T., Alnoor, A., et al.: Multi-criteria decisionmaking for coronavirus disease 2019 applications: a theoretical analysis review. Artif. Intell. Rev. 55(6), 4979–5062 (2022).

Lee, C.S., Tsai, Y.L., Wang, M.H., Huang, S.H., Reformat, M., Kubota, N.: Adaptive fuzzy neural agent for human and machine co-learning. Int. J. Fuzzy Syst. 24(2), 778–798 (2022). https://doi.org/10.1007/s40815-021-01188-6.

Li, Y., Chen, Y., Li, Q.: A clustering algorithm for triangular fuzzy normal random variables. Int. J. Fuzzy Syst. 22(7), 2083–2100 (2020). https://doi.org/10.1007/s40815-020-00933-7.

Biedma-Rdguez, C., Gacto, M.J., Anguita-Ruiz, A., Alcala´-Fdez, J., Alcala´, R.: Transparent but accurate evolutionary regression combining new linguistic fuzzy grammar and a novel interpretable linear extension. Int. J. Fuzzy Syst. 24(7), 3082–3103 (2022). https://doi.org/10.1007/s40815-022-01324-w.

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