Using Artificial Neural Nets for Statistical Discovery: Observations after Using Backpropogation, Expert Systems, and Multiple-Linear Regression on Clinical Trial Data

Powerful new training algori thms developed for ar tificial neural networks hold th e promise of identifying regulari ties in the t raining data and generalizing over the test data. T he backprop agation algorithm is one such training algorithm t ha t, with th e use of hidden units, can learn functions such as exclusive-or. These fun ctions can be learned by statis tical techniques such as multiple-linear regression only by introducing additional parameters . We report experimental comparisons of th e performance of backpropagation, multiplelinear regression, and an expert system. We conclude that, for the data studied here, backpropagation is unsuitable for discovering st atist ical relationships. It may be possible to customize neural-net algorithms for niche applicatio ns in discovery syst ems.