Citation

Tee, Connie and Tan, Yee Fan and Goh, Michael Kah Ong and Hon, Hock Woon and Kadim, Zulaikha and Wong, Li Pei (2022) Explainable health prediction from facial features with transfer learning. Journal of Intelligent & Fuzzy Systems, 42 (3). pp. 2491-2503. ISSN 1064-1246

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Abstract

In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model.

Item Type:	Article
Uncontrolled Keywords:	Explainable AI, health prediction, transfer learning, deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Mar 2022 02:43
Last Modified:	03 Mar 2022 02:43
URII:	http://shdl.mmu.edu.my/id/eprint/10022

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