A Proposed Artificial Neural Network Classifier to Identify Tumor Metastases : Part 1
by
Kuldeep Kumar
Associate Professor, Bond University, Gold Coast, Australia
Coauthors: Sukanto Bhattacharya (School of Information Technology, Bond University, Australia)

Recursive partitioning techniques are widely used in health data. In this paper we have reviewed various recursive partitioning techniques and propose a classification scheme to isolate truly benign tumors from those that initially start off as benign but subsequently show metastases. A non-parametric artificial neural network methodology has been chosen because of the analytical difficulties associated with extraction of closed-form stochastic-likelihood parameters given the extremely complicated and possibly non-linear behavior of the state variables. This is intended as the first of a three-part research output. In this paper, we have proposed and justified the computational schema. In the second part we shall set up a working model of our schema and pilot-test it with clinical data while in the concluding part we shall give an in-depth analysis of the numerical output and model findings and compare it to existing methods of tumor growth modeling and malignancy prediction.

Date received: November 13, 2002