Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare
Conference paper
Authors | Yu, H., Adebola Alaba and Ebere Eziefuna |
---|---|
Type | Conference 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. |
Keywords | Explainability AI; Machine learning ; Healthcare |
Year | 2024 |
Conference | International Workshop on Internet of Things of Big Data for Healthcare |
Publisher | Springer |
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 |
ISBN | 978-3-031-52216-1 |
Output status | Published |
Publication dates | 29 Jan 2024 |
Publication process dates | |
Deposited | 07 Mar 2024 |
https://repository.derby.ac.uk/item/q512x/evaluation-of-integrated-xai-frameworks-for-explaining-disease-prediction-models-in-healthcare
Restricted files
Accepted author manuscript
27
total views0
total downloads3
views this month0
downloads this month