Inducing diagnostic rules for glomerular disease with the DLG machine learning algorithm

Authors:
Geoffrey I Webb;John W. M Agar
Affiliations:
Department of Computing and Mathematics, Deakin University, Geelong, Victoria 3217, Australia;Geelong Hospital, Geelong, Victoria, Australia
Venue:
Artificial Intelligence in Medicine
Year:
1992

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Abstract

A pilot study has applied the DLG machine learning algorithm to create expert systems for the assessment and interpretation of clinical and laboratory data in glomerular disease. Despite the limited size of the data-set and major deficiencies in the information recorded therein, promising results have been obtained. On average, 100 expert systems developed from different subsets of the database, had a diagnostic accuracy of 54.70% when applied to cases that had not been used in their development. This compares with an average diagnostic accuracy of 48.31% obtained by four expert clinicians and of 3.23% obtained by random diagnosis. The expert systems demonstrated increased accuracy (62.90% on average) when cases of diseases represented by less than twenty examples were discarded. These results suggest that database expansion may enable the induction of diagnostic rules that provide accurate non-invasive diagnosis of specific categories of glomerular disease.