Abstract
Machine learning has several uses in healthcare, including medical diagnosis. The Wisconsin Breast Cancer Diagnosis (WBCD) dataset is a popular source of information for cancer researchers. Machine learning models used in healthcare, like those used in other fields, are still mostly unknown. Understanding the rationale behind machine learning model predictions is critical in deciding trust if a clinician wants to initiate cancer treatment action based on a prediction of diagnosis, as outlined in. Such comprehension can also assist clinicians with domain experience in detecting flaws in machine learning model predictions. This can be accomplished in a variety of ways. “Explainable machine learning” is one of the ways. In this research, We illustrate how to leverage the WBCD dataset to build explainable machine learning to make an untrustworthy prediction trustworthy. The book chapter includes a case study that will be very useful to get more exposure to the same and helpful for the researchers working in the same field.
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Whig, P., Kouser, S., Bhatia, A.B., Nadikattu, R.R., Sharma, P. (2023). Explainable Machine Learning in Healthcare. In: Hossain, M.S., Kose, U., Gupta, D. (eds) Explainable Machine Learning for Multimedia Based Healthcare Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-38036-5_5
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DOI: https://doi.org/10.1007/978-3-031-38036-5_5
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