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. 2013:2013:814967.
doi: 10.1155/2013/814967. Epub 2013 Oct 3.

Combining personality traits with traditional risk factors for coronary stenosis: an artificial neural networks solution in patients with computed tomography detected coronary artery disease

Angelo Compare  1 , Enzo Grossi, Massimo Buscema, Cristina Zarbo, Xia Mao, Francesco Faletra, Elena Pasotti, Tiziano Moccetti, Paula M C Mommersteeg, Angelo Auricchio
Affiliations

Combining personality traits with traditional risk factors for coronary stenosis: an artificial neural networks solution in patients with computed tomography detected coronary artery disease

Angelo Compare et al. Cardiovasc Psychiatry Neurol. 2013.

Abstract

Background. Coronary artery disease (CAD) is a complex, multifactorial disease in which personality seems to play a role but with no definition in combination with other risk factors. Objective. To explore the nonlinear and simultaneous pathways between traditional and personality traits risk factors and coronary stenosis by Artificial Neural Networks (ANN) data mining analysis. Method. Seventy-five subjects were examined for traditional cardiac risk factors and personality traits. Analyses were based on a new data mining method using a particular artificial adaptive system, the autocontractive map (AutoCM). Results. Several traditional Cardiovascular Risk Factors (CRF) present significant relations with coronary artery plaque (CAP) presence or severity. Moreover, anger turns out to be the main factor of personality for CAP in connection with numbers of traditional risk factors. Hidden connection map showed that anger, hostility, and the Type D personality subscale social inhibition are the core factors related to the traditional cardiovascular risk factors (CRF) specifically by hypertension. Discussion. This study shows a nonlinear and simultaneous pathway between traditional risk factors and personality traits associated with coronary stenosis in CAD patients without history of cardiovascular disease. In particular, anger seems to be the main personality factor for CAP in addition to traditional risk factors.

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Figures

Figure 1
Figure 1
Semantic connectivity map. Note: semantic connectivity map of the variables under study in the study group with AutoCM system. The values on the arcs of the graph indicate the strength of the connection, measured on a scale ranging from zero to 1.
Figure 2
Figure 2
Analysis of the hidden connections through Maximally Regular Graph.
Figure 3
Figure 3
Principal component analysis; first and second components.
See this image and copyright information in PMC

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