title: The prediction of coronary atherosclerosis employing artificial neural networks.
abstract: BACKGROUND
  Atherosclerosis is a complex histopathologic process that is analogous to chronic inflammatory conditions. Several factors have been shown to correlate with the extent of atherosclerosis. Whereas hypertension, obesity, hyperlipidemia, diabetes, smoking, and family history are all well documented, recent literature points to additional associated factors. Thus, antibodies to oxidized low-density lipoprotein (oxLDL), cytomegalovirus (CMV), Chlamydia pneumonia, Helicobacter pylori, as well as homocysteine and C-reactive protein (CRP) levels have all been implicated as independent markers of accelerated atherosclerosis.
  
  
  OBJECTIVE
  In the current study we attempted to formulate a system by which to predict the extent of coronary atherosclerosis as assessed by angiographic vessel occlusion.
  
  
  METHODS
  The 81 patients were categorized as having single-, double-, triple-, or no vessel involvement. The clinical data concerning the "classic" risk factors were obtained from clinical records, and sera were drawn from the patients for determination of the various parameters that are thought to be associated with atherosclerosis.
  
  
  RESULTS
  Using four artificial neural networks, we have found the most effective parameters predictive of coronary vessel involvement were (in decreasing order of importance) antibodies to oxLDL, to cardiolipin, to CMV, to Chlamydia pneumonia, and to beta 2-glycoprotein I (beta 2GPI). Although important in the prediction of vessel occlusion, hyperlipidemia, hypertension, CRP levels, and diabetes were less accurate.
  
  
  CONCLUSIONS
  The results of the current study, if reproduced in a larger population, may establish an integrated system based on the creation of artificial neural networks by which to predict the extent of atherosclerosis in a given subject fairly and noninvasively.
authors.affiliation: Department of Medicine B, Sheba Medical Center, Tel Hashomer, Israel.
authors.forename: J
authors.initials: J
authors.lastname: George
authors.forename: A
authors.initials: A
authors.lastname: Ahmed
authors.forename: M
authors.initials: M
authors.lastname: Patnaik
authors.forename: Y
authors.initials: Y
authors.lastname: Adler
authors.forename: Y
authors.initials: Y
authors.lastname: Levy
authors.forename: D
authors.initials: D
authors.lastname: Harats
authors.forename: B
authors.initials: B
authors.lastname: Gilburd
authors.forename: J
authors.initials: J
authors.lastname: Terrybery
authors.forename: G Q
authors.initials: GQ
authors.lastname: Shen
authors.forename: A
authors.initials: A
authors.lastname: Sagie
authors.forename: I
authors.initials: I
authors.lastname: Herz
authors.forename: P
authors.initials: P
authors.lastname: Snow
authors.forename: J
authors.initials: J
authors.lastname: Brandt
authors.forename: J
authors.initials: J
authors.lastname: Peter
authors.forename: Y
authors.initials: Y
authors.lastname: Shoenfeld
mesh_terms: D000368:Aged; D017023:Coronary Angiography; D003324:Coronary Artery Disease / Q000175:diagnosis* / Q000000981:diagnostic imaging; D018450:Disease Progression; D004797:Enzyme-Linked Immunosorbent Assay; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012307:Risk Factors