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Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model

Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model

Siddhartha Kumar Arjaria, Abhishek Singh Rathore, Gyanendra Chaubey
Copyright: © 2022 |Volume: 9 |Issue: 3 |Pages: 18
ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781683182894|DOI: 10.4018/IJBAN.292058
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MLA

Arjaria, Siddhartha Kumar, et al. "Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model." IJBAN vol.9, no.3 2022: pp.1-18. http://doi.org/10.4018/IJBAN.292058

APA

Arjaria, S. K., Rathore, A. S., & Chaubey, G. (2022). Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model. International Journal of Business Analytics (IJBAN), 9(3), 1-18. http://doi.org/10.4018/IJBAN.292058

Chicago

Arjaria, Siddhartha Kumar, Abhishek Singh Rathore, and Gyanendra Chaubey. "Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model," International Journal of Business Analytics (IJBAN) 9, no.3: 1-18. http://doi.org/10.4018/IJBAN.292058

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Abstract

Healthcare and medicine are key areas where machine learning algorithms are widely used. The medical decision support systems thus created are accurate enough, however, they suffer from the lack of transparency in decision making and shows a black box behavior. However, transparency and trust are significant in the field of health and medicine and hence, a black box system is sub optimal in terms of widespread applicability and reach. Hence, the explainablility of the research make the system reliable and understandable, thereby enhancing its social acceptability. The presented work explores a thyroid disease diagnosis system. SHAP, a popular method based on coalition game theory is used for interpretability of results. The work explains the system behavior both locally and globally and shows how machine leaning can be used to ascertain the causality of the disease and support doctors to suggest the most effective treatment of the disease. The work not only demonstrates the results of machine learning algorithms but also explains related feature importance and model insights.