A systematic approach to enhance the explainability of artificial intelligence in healthcare with application to diagnosis of diabetes

Yu Cheng Wang*, Tin Chih Toly Chen, Min Chi Chiu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Explainable artificial intelligence (XAI) tools are used to enhance the applications of existing artificial intelligence (AI) technologies by explaining their execution processes and results. In most past research, XAI tools and techniques are typically applied to only the inference part of the AI application. This study proposes a systematic approach to enhance the explainability of AI applications in healthcare. Several AI applications for type 2 diabetes diagnosis are taken as examples to illustrate the applicability of the proposed methodology. According to experimental results, the XAI tools and technologies in the proposed methodology were more diverse than those in the past research. In addition, an artificial neural network was approximated to a simpler and more intuitive classification and regression tree (CART) using local interpretable model-agnostic explanation (LIME). The extracted rules were used to recommend actions to the users to restore their health.

Original languageEnglish
Article number100183
JournalHealthcare Analytics
Volume3
DOIs
StatePublished - Nov 2023

Keywords

  • Artificial intelligence
  • Diabetes diagnosis
  • Explainable artificial intelligence
  • Healthcare
  • Local interpretable model-agnostic explanation

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