Accession Number : ADA258366


Title :   A Comparison of Multiple Regression and a Neural Network for Predicting a Medical Diagnosis.


Descriptive Note : Interim rept. Jul-Sep 91,


Corporate Author : NAVAL HEALTH RESEARCH CENTER SAN DIEGO CA


Personal Author(s) : Pugh, William M ; Ryman, David H


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a258366.pdf


Report Date : Oct 1991


Pagination or Media Count : 23


Abstract : Regression and neural network prediction methods were compared using artificial data generated to simulate three types of predictor-criterion relationships: linear, polynomial, and interactive. Analyses of linear data indicated that both methods were comparable on large data sets. On small data sets the neural network tended to overfit the initial data and thus did not generalize as well as the regression equation. Analysis of data with a non- linear component demonstrated the ability of the neural network to fit either a polynomial or interactive term without the user having to model such terms. However, when these effects were modeled, the regression equation permored well. The implications of these results for the development of predictive algorithms were discussed. Algorithms, regression equations, predictive algorithms, diagnostic algorithms, neural network.


Descriptors :   *ALGORITHMS , *NETWORKS , *DIAGNOSIS(MEDICINE) , PARAMETRIC ANALYSIS , POLYNOMIALS , COMPUTER APPLICATIONS , EQUATIONS , NAVAL RESEARCH , REGRESSION ANALYSIS , PREDICTIONS , MODELS


Subject Categories : Medicine and Medical Research
      Operations Research


Distribution Statement : APPROVED FOR PUBLIC RELEASE