Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters.
The aim of this study was to develop an artificial neural networks-based (ANNs) diagnostic model for coronary heart disease (CHD) using a complex of traditional and genetic factors of this disease. The original database for ANNs included clinical, laboratory, functional, coronary angiographic, and genetic [single nucleotide polymorphisms (SNPs)] characteristics of 487 patients (327 with CHD caused by coronary atherosclerosis, 160 without CHD). By changing the types of ANN and the number of input factors applied, we created models that demonstrated 64-94% accuracy. The best accuracy was obtained with a neural networks topology of multilayer perceptron with two hidden layers for models included by both genetic and non-genetic CHD risk factors.
N.I. Pirogov Russian State Medical University, Moscow, Russia.
Oleg Yu
OY
Atkov
Svetlana G
SG
Gorokhova
Alexandr G
AG
Sboev
Eduard V
EV
Generozov
Elena V
EV
Muraseyeva
Svetlana Y
SY
Moroshkina
Nadezhda N
NN
Cherniy
D017023:Coronary Angiography; D003327:Coronary Disease / Q000175:diagnosis* / Q000235:genetics; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011110:Polymorphism, Genetic; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors