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Analysis and interpretability of machine learning models to classify thyroid disease.

Sumya Akter1,2, Hossen A Mustafa1

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This study introduces a novel clustering-based data balancing technique for machine learning (ML) models in thyroid disease classification. The approach enhances diagnostic accuracy and provides interpretable results, addressing the "black box" issue in clinical applications.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Thyroid disease classification is vital for timely diagnosis and treatment.
  • Machine learning (ML) offers powerful tools for thyroid disease diagnosis.
  • Imbalanced datasets and lack of model interpretability are significant challenges in ML for healthcare.

Purpose of the Study:

  • To develop and evaluate a new data-balancing mechanism for ML-based thyroid disease classification.
  • To analyze the interpretability of various ML models using eXplainable Artificial Intelligence (XAI).
  • To bridge the gap between ML adoption and clinical transparency requirements.

Main Methods:

  • A novel clustering-based data-balancing technique was applied to imbalanced thyroid disease datasets.
  • Multiple ML algorithms were analyzed for classification performance.
  • eXplainable Artificial Intelligence (XAI) tools were used for global and local model explanation and feature importance analysis.
  • XAI findings were validated by domain experts.

Main Results:

  • The proposed data-balancing mechanism demonstrated efficiency in diagnosing thyroid diseases.
  • The ML models, when balanced using the new technique, showed improved performance.
  • XAI tools effectively explained model behavior and identified key features, with validation from experts.
  • The study successfully addressed the interpretability challenge in ML models for thyroid disease classification.

Conclusions:

  • The developed clustering-based data balancing method is effective for thyroid disease classification.
  • XAI techniques enhance the transparency and clinical applicability of ML models in diagnostics.
  • This work supports the integration of interpretable ML into clinical decision-making for thyroid disorders.