Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture
Article
Article Title | Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Joseph, Lionel P., Joseph, Erica A. and Prasad, Ramendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 151 (Part A) |
Article Number | 106178 |
Number of Pages | 22 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.106178 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482522008861 |
Abstract | Diabetes is a deadly chronic disease that occurs when the pancreas is not able to produce ample insulin or when the body cannot use insulin effectively. If undetected, it may lead to a host of health complications. Hence, accurate and explainable early-stage detection of diabetes is essential for the proper administration of treatment options in leading a healthy and productive life. For this, we developed an interpretable TabNet model tuned via Bayesian optimization (BO). To achieve model-specific interpretability, the attention mechanism of TabNet architecture was used, which offered the local and global model explanations on the influence of the attributes on the outcomes. The model was further explained locally and globally using more robust model-agnostic LIME and SHAP eXplainable Artificial Intelligence (XAI) tools. The proposed model outperformed all benchmarked models by obtaining high accuracy of 92.2% and 99.4% using the Pima Indians diabetes dataset (PIDD) and the early-stage diabetes risk prediction dataset (ESDRPD), respectively. Based on the XAI results, it was clear that the most influential attribute for diabetes classification using PIDD and ESDRPD were Insulin and Polyuria, respectively. The feature importance values registered for insulin was 0.301 (PIDD) and for polyuria 0.206 was registered (ESDRPD). The high accuracy and ancillary interpretability of our objective model is expected to increase end-users trust and confidence in early-stage detection of diabetes. |
Keywords | Attention mechanism ; “Black-box” models ; Diabetes classification ; Bayesian optimization ; eXplainable artificial intelligence (XAI) ; TabNet; Interpretability |
ANZSRC Field of Research 2020 | 4699. Other information and computing sciences |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Mathematics, Physics and Computing |
University of Fiji, Fiji |
https://research.usq.edu.au/item/z022x/explainable-diabetes-classification-using-hybrid-bayesian-optimized-tabnet-architecture
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