Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture

Article


Joseph, Lionel P., Joseph, Erica A. and Prasad, Ramendra. 2022. "Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture." Computers in Biology and Medicine. 151 (Part A). https://doi.org/10.1016/j.compbiomed.2022.106178
Article Title

Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture

ERA Journal ID5040
Article CategoryArticle
AuthorsJoseph, Lionel P., Joseph, Erica A. and Prasad, Ramendra
Journal TitleComputers in Biology and Medicine
Journal Citation151 (Part A)
Article Number106178
Number of Pages22
Year2022
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN0010-4825
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.

KeywordsAttention mechanism ; “Black-box” models ; Diabetes classification ; Bayesian optimization ; eXplainable artificial intelligence (XAI) ; TabNet; Interpretability
ANZSRC Field of Research 20204699. Other information and computing sciences
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Byline AffiliationsSchool of Mathematics, Physics and Computing
University of Fiji, Fiji
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