A Neural Network Based Model for Prognosis of Early Breast Cancer

The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Survival estimations are currently performed by clinicians using non-numerical techniques. Artificial neural networks are shown to be a powerful tool for analyzing data sets where there are complicated nonlinear interactions between the input data and the information to be predicted. In this paper, a new estimation to set the maximum bound on prediction accuracy is presented, based on the approximation of the a posteriori probability of Bayes by feed-forward three-layer neural networks. This result is applied to different patients' follow-up time intervals, in order to obtain the best prediction accuracy for the correct classification probability of patient relapse after breast cancer surgery using clinical-pathological data (tumor size, patient age, menarchy age, etc.), which were obtained from the Medical Oncology Service of the Hospital Clinico Universitario of Malaga, Spain. Different network topologies and learning parameters are investigated to obtain the best prediction accuracy. The actual results show as, after training process, the final model is appropriate to make predictions about the relapse probability at different times of follow-up.

[1]  W. Baxt.  Application of artificial neural networks to clinical medicine , 1995, The Lancet.

[2]  M F Jefferson,et al.  Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma , 1997, Cancer.

[3]  M. O'Neill,et al.  Training back-propagation neural networks to define and detect DNA-binding sites. , 1991, Nucleic acids research.

[4]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[5]  Ken-ichi Funahashi,et al.  Multilayer neural networks and Bayes decision theory , 1998, Neural Networks.

[6]  Dan W. Patterson,et al.  Artificial Neural Networks: Theory and Applications , 1998 .

[7]  T. Sejnowski,et al.  Predicting the secondary structure of globular proteins using neural network models. , 1988, Journal of molecular biology.

[8]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[9]  Klaus-Robert Müller,et al.  Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective? , 1995, NIPS.

[10]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[11]  Raúl Rojas,et al.  A Short Proof of the Posterior Probability Property of Classifier Neural Networks , 1996, Neural Computation.

[12]  Torsten Söderström,et al.  Model structure selection for multivariable systems by cross-validation methods , 1987 .

[13]  G. Clark,et al.  A practical application of neural network analysis for predicting outcome of individual breast cancer patients , 2005, Breast Cancer Research and Treatment.

[14]  D.,et al.  Regression Models and Life-Tables , 2022 .

[15]  Terrence J. Sejnowski,et al.  Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.

[16]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[17]  G M Clark,et al.  How to use prognostic factors in axillary node-negative breast cancer patients. , 1990, Journal of the National Cancer Institute.

[18]  Richard M. Golden,et al.  Mathematical Methods for Neural Network Analysis and Design , 1996 .