An XAI Based Autism Detection: The Context Behind the Detection

被引：45

作者：

Biswas, Milon ^{[1
]}

Kaiser, M. Shamim ^{[2
,3
]}

Mahmud, Mufti ^{[4
]}

Al Mamun, Shamim ^{[2
,3
]}

Hossain, Md Shahadat ^{[5
]}

Rahman, Muhammad Arifur ^{[3
,6
]}

机构：

[1] Bangladesh Univ Business & Technol, Dept Comp Sci & Engn, Dhaka 1216, Bangladesh

[2] Jahangirnagar Univ, Inst Informat, Dhaka 1342, Bangladesh

[3] Jahangirnagar Univ, Wazed Miah Sci Res Ctr, Appl Intelligence & Informat AII Lab, Dhaka 1342, Bangladesh

[4] Nottingham Trent Univ, Dept Comp Sci, Clifton Lane, Nottingham NG118NS, England

[5] Chittagong Univ, Dept Comp Sci & Engn, Chittagong 4331, Bangladesh

[6] Jahangirnagar Univ, Dept Phys, Dhaka 1342, Bangladesh

来源：

BRAIN INFORMATICS, BI 2021 | 2021年 / 12960卷

关键词：

Explainable AI; Support vector machine (SVM); Machine learning; Co-relation coefficient;

D O I：

10.1007/978-3-030-86993-9_40

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the rapid growth of the Internet of Healthcare Things, a massive amount of data is generated by a broad variety of medical devices. Because of the complex relationship in large-scale healthcare data, researchers who bring a revolution in the healthcare industry embrace Artificial Intelligence (AI). In certain cases, it has been reported that AI can do better than humans at performing healthcare tasks. The data-driven black-box model, on the other hand, does not appeal to healthcare professionals as it is not transparent, and any biasing can hamper the performance the prediction model for the real-life operation. In this paper, we proposed an AI model for early detection of autism in children. Then we showed why AI with explainability is important. This paper provides examples focused on the Autism Spectrum Disorder dataset (Autism screening data for toddlers by Dr Fadi Fayez Thabtah) and discussed why explainability approaches should be used when using AI systems in healthcare.

引用

页码：448 / 459

页数：12

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