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Article


Title

A Comparative Study of Two Rule-Based Explanation Methods for Diabetic Retinopathy Risk Assessment

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

Applied Sciences

Journal year: 2022 | Journal volume: vol. 12 | Journal number: iss. 7

Article type

scientific article

Publication language

english

Keywords
EN
  • explainable AI
  • machine learning
  • fuzzy rules
  • dominance-based rough set approach
  • diabetic retinopathy
Abstract

EN Understanding the reasons behind the decisions of complex intelligent systems is crucial in many domains, especially in healthcare. Local explanation models analyse a decision on a single instance, by using the responses of the system to the points in its neighbourhood to build a surrogate model. This work makes a comparative analysis of the local explanations provided by two rule-based explanation methods on RETIPROGRAM, a system based on a fuzzy random forest that analyses the health record of a diabetic person to assess his/her degree of risk of developing diabetic retinopathy. The analysed explanation methods are C-LORE-F (a variant of LORE that builds a decision tree) and DRSA (a method based on rough sets that builds a set of rules). The explored methods gave good results in several metrics, although there is room for improvement in the generation of counterfactual examples.

Date of online publication

25.03.2022

Pages (from - to)

3358-1 - 3358-18

DOI

10.3390/app12073358

URL

https://www.mdpi.com/2076-3417/12/7/3358

Comments

Article Number: 3358

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Points of MNiSW / journal

100.0

Impact Factor

2.679 [List 2020]

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