Selection of artificial neural network models for survival analysis with Genetic Algorithms

Federico Ambrogi, Nicola Lama, Patrizia Boracchi, Elia Biganzoli

Research output: Contribution to journalArticlepeer-review


In follow-up clinical studies, the main time end-point is the failure from a specific starting point (e.g. treatment, surgery). A deeper investigation concerns the causes of failure. Statistical analysis typically focuses on the study of the cause specific hazard functions of possibly censored survival data. In the framework of discrete time models and competing risks, a multilayer perceptron was already proposed as an extension of generalized linear models with multinomial errors using a non-linear predictor (PLANNCR). According to standard practice, weight-decay was adopted to modulate model complexity. A Genetic Algorithm is considered for the complexity control of PLANNCR allowing to regularize independently each parameter of the model. The ICOMP information criterion is used as fitness function. To demonstrate the criticality and the benefits of the technique an application to a case series of 1793 women with primary breast cancer without axillary lymph node involvement is presented.

Original languageEnglish
Pages (from-to)30-42
Number of pages13
JournalComputational Statistics and Data Analysis
Issue number1
Publication statusPublished - Sep 15 2007


  • Competing risks
  • Genetic Algorithms
  • Neural networks
  • Regularization

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Numerical Analysis
  • Statistics and Probability


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