Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation

Yumnah Hasan, Allan de Lima, Fatemeh Amerehi, Darian Reyes Fernández de Bulnes, Patrick Healy, Conor Ryan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Medical imaging diagnosis increasingly relies on Machine Learning (ML) models. This is a task that is often hampered by severely imbalanced datasets, where positive cases can be quite rare. Their use is further compromised by their limited interpretability, which is becoming increasingly important. While post-hoc interpretability techniques such as SHAP and LIME have been used with some success on so-called black box models, the use of inherently understandable models makes such endeavours more fruitful. This paper addresses these issues by demonstrating how a relatively new synthetic data generation technique, STEM, can be used to produce data to train models produced by Grammatical Evolution (GE) that are inherently understandable. STEM is a recently introduced combination of the Synthetic Minority Oversampling Technique (SMOTE), Edited Nearest Neighbour (ENN), and Mixup; it has previously been successfully used to tackle both between-class and within-class imbalance issues. We test our technique on the Digital Database for Screening Mammography (DDSM) and the Wisconsin Breast Cancer (WBC) datasets and compare Area Under the Curve (AUC) results with an ensemble of the top three performing classifiers from a set of eight standard ML classifiers with varying degrees of interpretability. We demonstrate that the GE-derived models present the best AUC while still maintaining interpretable solutions.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Proceedings
EditorsStephen Smith, João Correia, Christian Cintrano
PublisherSpringer Science and Business Media Deutschland GmbH
Pages224-239
Number of pages16
ISBN (Print)9783031568510
DOIs
Publication statusPublished - 2024
Event27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024 - Aberystwyth, United Kingdom
Duration: 3 Apr 20245 Apr 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14634 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024
Country/TerritoryUnited Kingdom
CityAberystwyth
Period3/04/245/04/24

Keywords

  • Augmentation
  • Breast Cancer
  • Ensemble
  • Grammatical Evolution
  • STEM

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