[Formation of diagnostic rules by using neural networks].
Neural networks were trained on unrepresentative patient data sets proposed by skilled clinicians. In all cases, the trained neural networks correctly classified all presented cases. There were 2 intervals introduced for encoding estimates. The number of both signs and neurons in the trained neural networks was minimized. The trained neural networks adequately represent a set of logical formulas that are easy to perceive. These formulas are similar to syndrome complexes, they can be tabulated and presented as diagnostic tables the physicians usually use. The proposed decisions are accompanied by the estimates of their affinity, which allows their validity to be controlled. The clinical studies performed showed that the proposed decisions were in good agreement with medical conclusions in most cases (over 88%).
Shchetinin
V G
VG
Brazhnikov
A I
AI
D009202:Cardiomyopathies / Q000175:diagnosis; D002908:Chronic Disease; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D004697:Endocarditis, Bacterial / Q000175:diagnosis; D006801:Humans; D007938:Leukemia / Q000175:diagnosis; D008180:Lupus Erythematosus, Systemic / Q000175:diagnosis; D008962:Models, Theoretical; D009205:Myocarditis / Q000175:diagnosis; D016571:Neural Networks, Computer; D011183:Postoperative Complications / Q000175:diagnosis; D012216:Rheumatic Diseases / Q000175:diagnosis; D013997:Time Factors