Inductive learning of thyroid functional states using the ID3 algorithm. The effect of poor examples on the learning result.
The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data.
Forsström
J
J
Department of Clinical Chemistry, University of Turku, Finland.
D000465:Algorithms; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006801:Humans; D013959:Thyroid Diseases / Q000097:blood / Q000175:diagnosis*; D013960:Thyroid Function Tests; D013972:Thyrotropin / Q000097:blood; D013974:Thyroxine / Q000097:blood
D013972:Thyrotropin; D013974:Thyroxine