A Comparative Study of Two Rule-Based Explanation Methods for Diabetic Retinopathy Risk Assessment
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] pracownik
2022
artykuł naukowy
angielski
- explainable AI
- machine learning
- fuzzy rules
- dominance-based rough set approach
- diabetic retinopathy
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.
25.03.2022
3358-1 - 3358-18
Article Number: 3358
CC BY (uznanie autorstwa)
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