title: A comparison of multi-layer neural network and logistic regression in hereditary non-polyposis colorectal cancer risk assessment.
abstract: Hereditary non-polyposis colorectal cancer (HN-PCC) is one of the most common autosomal dominant diseases in developed countries. Here, we report on a system to identify the risk of a family having HNPCC based on its history. This is important since population-wide genetic screening for HNPCC is not currently considered feasible due to its complexity and expense. If the risk of a family having HNPCC can be identified/asessed, then only the high risk fraction of the population would undergo intensive screening. Here, we have developed a Multi-Layer Feed-Forward Neural Network to classify families into high-, intermediate- and low-risk categories and compared the result with the benchmark logistic regression model.
authors.affiliation: BIOCORE, School of MIS, Coventry University, Coventry, UK.
authors.forename: Munevver
authors.initials: M
authors.lastname: Kokuer
authors.forename: Raouf
authors.initials: R
authors.lastname: G Naguib
authors.forename: Peter
authors.initials: P
authors.lastname: Jancovic
authors.forename: H
authors.initials: H
authors.lastname: Banfield Younghusband
authors.forename: Roger
authors.initials: R
authors.lastname: Green