Explaining multivariate molecular diagnostic tests via Shapley values.

ABSTRACT:

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

SUBMITTER: Roder J 

PROVIDER: S-EPMC8265031 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature