A combined neural network and decision trees model for prognosis of breast cancer relapse.

The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Different prognostic factors for breast cancer outcome appear to be significant predictors for overall survival, but probably form part of a bigger picture comprising many factors. Survival estimations are currently performed by clinicians using the statistical techniques of survival analysis. In this sense, artificial neural networks are shown to be a powerful tool for analysing datasets where there are complicated non-linear interactions between the input data and the information to be predicted. This paper presents a decision support tool for the prognosis of breast cancer relapse that combines a novel algorithm TDIDT (control of induction by sample division method, CIDIM), to select the most relevant prognostic factors for the accurate prognosis of breast cancer, with a system composed of different neural networks topologies that takes as input the selected variables in order for it to reach good correct classification probability. In addition, a new method for the estimate of Bayes' optimal error using the neural network paradigm is proposed. Clinical-pathological data were obtained from the Medical Oncology Service of the Hospital Clinico Universitario of Málaga, Spain. The results show that the proposed system is an useful tool to be used by clinicians to search through large datasets seeking subtle patterns in prognostic factors, and that may further assist the selection of appropriate adjuvant treatments for the individual patient.