Use of artificial neural networks in modeling associations of discriminant factors: towards an intelligent selective breast cancer screening.
In order to improve the costs/benefits ratio of breast cancer (BC) screenings, the author evaluated the performance of a back-propagation artificial neural network (ANN) to predict an outcome (cancer/not cancer) to be used as classificator. Networks were trained on data from familial history of cancer, and sociodemographic, gynecoobstetric and dietary variables. The ANN achieved up to 94.04% of positive predictive value and 97.60% of negative predictive value. Results could operate as guidelines for preselecting women who would be considered as a BC high-risk subpopulation. The procedure--not only based on age factor, but on a multifactorial basis--appears to be a promising method of achieving a more efficient detection of preclinical, asymptomatic BC cases.
Registro Nacional de Cáncer, Montevideo, Uruguay. alronco@redfacil.com.uy
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D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008403:Mass Screening / Q000379:methods; D016571:Neural Networks, Computer