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Automated detection of hereditary syndromes using data mining

S Evans1, S J Lemon, C A Deters

  • 1Hereditary Cancer Institute, Creighton University School of Medicine, Omaha, NE, USA.

Computers and Biomedical Research, an International Journal
|February 11, 1998
PubMed
Summary

Computer-based data mining accurately identifies hereditary disease patterns from family history data. This method enhances patient selection for genetic studies, improving the efficiency of gene testing for hereditary colon cancer.

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Area of Science:

  • Computational biology
  • Genetics
  • Clinical informatics

Background:

  • Family history is crucial for identifying hereditary diseases.
  • Accurate pattern recognition in clinical data is challenging.
  • Existing methods may not efficiently select patients for genetic testing.

Purpose of the Study:

  • To develop and validate a data mining methodology for recognizing hereditary disease patterns.
  • To improve the accuracy and efficiency of patient selection for genetic studies.
  • To create an expert system linking genetic mutations to clinical disease presentation variations.

Main Methods:

  • Applied computer-based data mining to family history clinical data.
  • Developed algorithmic pattern recognizers for hereditary diseases, exemplified by colon cancer.

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  • Utilized statistical significance (P < 0.05) for factor selection.
  • Validated recognizer outputs with a clinical expert.

    • Main Results:

    • The data mining methodology achieved high accuracy in identifying hereditary disease patterns.
    • Statistically significant factors were selected for hereditary colon cancer assessment.
    • The recognizer correctly identified definitive hereditary histories and excluded non-hereditary cases.
    • The system demonstrated effectiveness in distinguishing hereditary from non-hereditary risk situations.

    Conclusions:

    • Computer-based data mining offers a powerful tool for creating accurate hereditary disease recognizers.
    • This approach significantly aids in selecting appropriate patients for DNA studies and gene mutation analysis.
    • Integrating genetic mutations into patient databases enhances expert systems for disease characterization.
    • The methodology improves the overall efficiency and precision of genetic testing.