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Identifying diagnostic errors with induced decision trees.

C K Murphy1

  • 1Penn State York, Pennsylvania 17403, USA. cxm53@psu.edu

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|September 29, 2001
PubMed
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Induced decision trees show higher accuracy in breast cancer diagnosis than pruned neural networks. Combining models can improve malignancy identification and describe ambiguous cases, enhancing diagnostic interpretation.

Area of Science:

  • Medical informatics
  • Machine learning in healthcare
  • Oncology

Background:

  • Accurate breast cancer diagnosis is crucial for effective treatment.
  • Fine-needle aspirate readings are a key diagnostic tool.
  • Interpreting diagnostic data can be challenging, leading to potential misclassifications.

Purpose of the Study:

  • To compare the diagnostic accuracy of induced decision trees against pruned neural networks for breast cancer.
  • To enhance the accuracy and interpretability of breast cancer diagnosis using fine-needle aspirate readings.
  • To identify cases prone to misclassification by induced decision rules.

Main Methods:

  • Utilized an online database of 699 suspected breast cancer cases.
  • Induced decision trees from a randomly selected half of the data; tested accuracy on the remaining cases.

Related Experiment Videos

  • Examined error patterns and created a dataset of misclassified cases to induce descriptive decision trees.

    • Main Results:

    • Larger, less pruned decision trees demonstrated superior accuracy for both training and test data.
    • Induced decision trees outperformed smaller pruned neural networks in diagnostic accuracy.
    • Combining classifications from multiple trees improved identification of missed malignancies, though inductive error analysis did not reduce the overall error rate.

    Conclusions:

    • Overly compact models may miss minority class cases, such as malignancies.
    • Combining diagnostic models and analyzing classification errors can improve breast cancer diagnosis.
    • New methods can enhance malignancy detection and provide descriptions for ambiguous cases.