  Explainable Artificial Intelligence (XAI) is a rising field in AI. It aims to
produce a demonstrative factor of trust, which for human subjects is achieved
through communicative means, which Machine Learning (ML) algorithms cannot
solely produce, illustrating the necessity of an extra layer producing support
to the model output. When approaching the medical field, we can see challenges
arise when dealing with the involvement of human-subjects, the ideology behind
trusting a machine to tend towards the livelihood of a human poses an ethical
conundrum - leaving trust as the basis of the human-expert in acceptance to the
machines decision. The aim of this paper is to apply XAI methods to demonstrate
the usability of explainable architectures as a tertiary layer for the medical
domain supporting ML predictions and human-expert opinion, XAI methods produce
visualization of the feature contribution towards a given models output on both
a local and global level. The work in this paper uses XAI to determine feature
importance towards high-dimensional data-driven questions to inform
domain-experts of identifiable trends with a comparison of model-agnostic
methods in application to ML algorithms. The performance metrics for a
glass-box method is also provided as a comparison against black-box capability
for tabular data. Future work will aim to produce a user-study using metrics to
evaluate human-expert usability and opinion of the given models.
