Title :
Neural-network-based decision making in diagnostic applications
Author_Institution :
Dept. of Comput. Eng., Bogazici Univ., Istanbul, Turkey
Abstract :
We discuss the neural network (NN) approach as a new way of looking at the problem of decision making, and we illustrate our discussion with two examples from gynaecology and obstetrics: assessment of intrauterine growth retardation, and assessment of luteinizing hormone surge for predicting ovulation time. We use the noisy test measurements and estimate the rest of the values by the interpolative property of the NN structure. We discuss the following issues: the meaning of dependence and independence of tests in medicine; the NN approach in combination of multiple gynaecology and obstetric tests in order to make medical decisions: a comparison of the NN approach to statistical decision making in medical decisions
Keywords :
backpropagation; decision theory; feedforward neural nets; gynaecology; medical expert systems; multilayer perceptrons; obstetrics; backpropagation; dependence of tests; feedforward neural net; gynaecology; independence of tests; interpolative property; intrauterine growth retardation assessment; luteinizing hormone surge assessment; medical diagnostic applications; multilayer perceptron; neural-network-based decision making; noisy test measurements; obstetrics; ovulation time prediction; Decision making; Diseases; Gynaecology; Intelligent networks; Medical diagnosis; Medical diagnostic imaging; Medical tests; Neural networks; Proposals; Testing;
Journal_Title :
Engineering in Medicine and Biology Magazine, IEEE
