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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Explainable AI-based feature importance analysis for ovarian cancer classification with ensemble methods.

Ashwini Kodipalli1,2, V Susheela Devi1, Shyamala Guruvare3

  • 1Department of Computer Science and Automation, Indian Institute of Science, Bangalore, Karnataka, India.

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|April 10, 2025
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Summary

This study introduces an AI diagnostic system for ovarian cancer (OC) detection, achieving 98.66% accuracy. Explainable AI methods provide insights, potentially improving early diagnosis and patient outcomes globally.

Keywords:
Cohen’sSHAPbaggingboostingensemble modelsinterpretable AImachine learningp-value

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Ovarian cancer (OC) is a leading cause of cancer deaths in women.
  • Early-stage OC diagnosis is challenging due to non-specific symptoms.
  • Current treatments offer marginal diagnostic improvements.

Purpose of the Study:

  • To design a computer-aided diagnostic system for accurate ovarian cancer classification and detection.
  • To utilize ensemble machine learning and explainable AI for deeper insights into diagnostic patterns.

Main Methods:

  • Developed a three-stage ensemble model incorporating a game-theoretic approach with SHAP values.
  • Evaluated and visualized results to identify key predictive features.
  • Validated SHAP values using statistical methods (p-test, Cohen's d-test).

Main Results:

  • Achieved a high diagnostic accuracy of 98.66%.
  • Demonstrated the model's consistency and advantages over single classifiers.
  • Validated feature importance using p-values and Cohen's d-values.

Conclusions:

  • The AI-based method accurately detects, diagnoses, and prognoses OC using multi-modal data.
  • The approach mimics clinical decision-making, offering reliable and consistent AI solutions.
  • Potential for improved patient outcomes, reduced costs, morbidity, and mortality, especially in resource-constrained settings.