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Explaining multivariate molecular diagnostic tests via Shapley values by Joanna Roder; Laura Maguire; Robert Georgantas; Heinrich Roder is a Health Professions article available to read on EtoBox.

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## Background Machine learning (ML) can be an effective tool to extract information from attribute-rich molecular datasets for the generation of molecular diagnostic tests. However, the way in which the resulting scores or classifications are produced from the input data may not be transparent. Algorithmic explainability or interpretability has become a focus of ML research. Shapley values, first introduced in game theory, can provide explanations of the result generated from a specific set of input data by a complex ML algorithm. ## Methods For a multivariate molecular diagnostic test in clinical use (the VeriStrat® test), we calculate and discuss the interpretation of exact Shapley values. We also employ some standard approximation techniques for Shapley value computation (local interpretable model-agnostic explanation (__LIME__) and Shapley Additive Explanations (__SHAP__) based methods) and compare the results with exact Shapley values. ## Results Exact Shapley values calculated for data collected from a cohort of 256 patients showed that the relative importance of attributes for test classification varied by sample. While all eight features used in the VeriStrat® test contribu

Who reads Explaining multivariate molecular diagnostic tests via Shapley values?

Author

Joanna Roder; Laura Maguire; Robert Georgantas; Heinrich Roder

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Health Professions (Health Sciences)