Construction and Evaluation of Robust Interpretation Models for Breast Cancer Metastasis Prediction

Nahim Adnan, Maryam Zand, Tim H.M. Huang, Jianhua Ruan

Research output: Contribution to journalArticlepeer-review

Abstract

Interpretability of machine learning (ML) models represents the extent to which a model's decision-making process can be understood by model developers and/or end users. Transcriptomics-based cancer prognosis models, for example, while achieving good accuracy, are usually hard to interpret, due to the high-dimensional feature space and the complexity of models. As interpretability is critical for the transparency and fairness of ML models, several algorithms have been proposed to improve the interpretability of arbitrary classifiers. However, evaluation of these algorithms often requires substantial domain knowledge. Here, we propose a breast cancer metastasis prediction model using a very small number of biologically interpretable features, and a simple yet novel model interpretation approach that can provide personalized interpretations. In addition, we contributed, to the best of our knowledge, the first method to quantitatively compare different interpretation algorithms. Experimental results show that our model not only achieved competitive prediction accuracy, but also higher inter-classifier interpretation consistency than state-of-the-art interpretation methods. Importantly, our interpretation results can improve the generalizability of the prediction models. Overall, this work provides several novel ideas to construct and evaluate interpretable ML models that can be valuable to both the cancer machine learning community and related application domains.

Original languageEnglish (US)
Pages (from-to)1344-1353
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume19
Issue number3
DOIs
StatePublished - 2022

Keywords

  • Cancer metastasis
  • feature engineering
  • interpretable machine learning
  • performance evaluation

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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