Knowledge-based system for prognosis of specific types of cancer using Elman neural network

Abstract

This paper focuses on the classification problem of high dimensional patterns and especially of socio-demographic cancer questionnaires. The purpose of this study is to define a predictive indicator of a published clinical study regarding the influence of Hormone Replacement Therapy (HRT) on the growth of cancers, including breast, ovarian, endometrial, and colon cancers. The proposed study, in the preparation stage, combines independent factors of this research using a Bayesian model in order to achieve a normalizing data linked by significant relevant properties of these factors. The specific goal is to determine a standard threshold value in which an independent self-organizing system will decide the correlation between the normalizing data of the preprocessing stage via a well-fitted, recurrent Elman neural network using a threshold factor which is called the distance value. A case study involving a dataset of published clinical research is used and the evaluated procedure is implemented by a well-fitted t-test control.