Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare

Conference paper


Yu, H., Adebola Alaba and Ebere Eziefuna 2024. Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_2
AuthorsYu, H., Adebola Alaba and Ebere Eziefuna
TypeConference paper
Abstract

Explainable AI was created to solve the black box problems of deep learning models. Various explainable AI algorithms can highlight the features used by a black box model in deciding. Integrated frameworks were developed with multiple machine learning capabilities including model explanation. This research aims to investigate which of the integrated frameworks is more implementable within the healthcare sector and to evaluate how well the selected frameworks can explain prediction done with a tabular, natural language processing and image dataset. We investigated OmniXAI and InterpretML frameworks and the selected explainers are LIME and SHAP. Prostate cancer, pneumonia chest x-ray and medical question and answer were used as the datasets for tabular, image and natural language processing predictions. The feature importance score was used to compare and evaluate the explanations of the algorithms. Findings show that OmniXAI supports more methods and data formats. It can implement and explain tabular, image and NLP predictions while InterpretML supports only tabular data. InterpretML had a better plot of the explanation for the tabular prediction. InterpretML is more user-friendly and easily implementable. The OmniXAI integrated framework can be used in laboratories where tests are carried out with results stored in text, image, or CSV format. As the results are fed into the models for prediction, the explainers of the integrated frameworks will give insights into the model predictions. InterpretML can be implemented in laboratories where tabular results are been generated.

KeywordsExplainability AI; Machine learning ; Healthcare
Year2024
ConferenceInternational Workshop on Internet of Things of Big Data for Healthcare
PublisherSpringer
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-031-52216-1_2
Web address (URL)https://doi.org/10.1007/978-3-031-52216-1_2
Accepted author manuscript
License
All rights reserved
File Access Level
Controlled
ISBN978-3-031-52216-1
Output statusPublished
Publication dates29 Jan 2024
Publication process dates
Deposited07 Mar 2024
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