Automated detection of hereditary syndromes using data mining

Steven Evans, Stephen J. Lemon, Carolyn A. Deters, Ramon M. Fusaro, Henry T. Lynch

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Computer-based data mining methology applied to family history clinical data can algorithmically create highly accurate, clinically oriented hereditary disease pattern recognizers. For the example of hereditary colon cancer, the data mining's selection of relevant factors to assess for hereditary colon cancer was statistically significant (P <0.05). All final recognizer-formulated patterns of hereditary colon cancer were independently confirmed by a clinical expert. Applied to previously analyzed family histories, the recognizer identified the definitive hereditary histories, correctly responded negatively to the putative hereditary histories, and correctly responded negatively to empirically elevated colon cancer risk situations. This capability facilitates patient selection for DNA studies in search of gene mutations. When genetic mutations are included as parameters in a patient database for a genetic disease the process yields an expert system which characterizes variations in clinical disease presentations in terms of genetic mutations. Such information can greatly improve the efficiency of gene testing.

Original languageEnglish (US)
Pages (from-to)337-348
Number of pages12
JournalComputers and Biomedical Research
Volume30
Issue number5
DOIs
StatePublished - Oct 1997

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All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)

Cite this

Evans, S., Lemon, S. J., Deters, C. A., Fusaro, R. M., & Lynch, H. T. (1997). Automated detection of hereditary syndromes using data mining. Computers and Biomedical Research, 30(5), 337-348. https://doi.org/10.1006/cbmr.1997.1454