Assessment of the influence of features on a classification problem: an application to COVID-19 patients

TL;DR

This paper introduces a Shapley value-based measure to evaluate feature influence in classification problems, validated through experiments and applied to COVID-19 patient data to assess risk factors.

Contribution

It presents a novel, axiomatic measure of feature influence using Shapley values, with validation and application to COVID-19 data.

Findings

The measure effectively quantifies feature influence.

Application reveals key demographic and risk factors.

Validated through experiments.

Abstract

This paper deals with an important subject in classification problems addressed by machine learning techniques: the evaluation of the influence of each of the features on the classification of individuals. Specifically, a measure of that influence is introduced using the Shapley value of cooperative games. In addition, an axiomatic characterisation of the proposed measure is provided based on properties of efficiency and balanced contributions. Furthermore, some experiments have been designed in order to validate the appropriate performance of such measure. Finally, the methodology introduced is applied to a sample of COVID-19 patients to study the influence of certain demographic or risk factors on various events of interest related to the evolution of the disease.