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In this work, we analyzed only the patients of the NSTEMI (non ST-segment elevation myocardial infarction) who arrived at the hospital within 12 h after symptoms started. Using NSTEMI follow-up data within, the characteristics of the clinical data, the risk factor, and the blood tested in the hospital visit were analyzed for MACE (major adverse cardiac events) patients. MACE includes cardiac death, MI (myocardial infarction), Re-PCI, and CABG (coronary artery bypass graft). As a result, from the NSTEMI patients which can be followed up for over 12 m, NT-ProBNP (p=0.014) and age (p=0.045) are found to be the independent risk factors related to MACE. Accordingly, they can be useful for the diagnosis and prognosis for NSTEMI patients as a biomarker.

In present study, we proposed not only a novel methodology useful in developing the various features of heart rate variability (HRV), but also a suitable prediction model to enhance the reliability of medical examinations and treatments for coronary artery disease. In order to develop the various features of HRV, we analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on linear and nonlinear features of HRV. Forty-three control subjects and 64 patients with coronary artery disease participated in this study. In order to extract various features, we tested five classification methods and evaluated performance of classifiers. As a result, SVM and CMAR (gave about 72---88% goodness of accuracy) outperformed the other classifiers.