@inproceedings{biswas_xai_2021,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {An {XAI} {Based} {Autism} {Detection}: {The} {Context} {Behind} the {Detection}},
	isbn = {978-3-030-86993-9},
	shorttitle = {An {XAI} {Based} {Autism} {Detection}},
	doi = {10.1007/978-3-030-86993-9_40},
	abstract = {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.},
	language = {en},
	booktitle = {Brain {Informatics}},
	publisher = {Springer International Publishing},
	author = {Biswas, Milon and Kaiser, M. Shamim and Mahmud, Mufti and Al Mamun, Shamim and Hossain, Md. Shahadat and Rahman, Muhammad Arifur},
	editor = {Mahmud, Mufti and Kaiser, M. Shamim and Vassanelli, Stefano and Dai, Qionghai and Zhong, Ning},
	year = {2021},
	keywords = {Co-relation coefficient, Explainable AI, Machine learning, Support vector machine (SVM)},
	pages = {448--459},
}

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