Objective: The primary aim of this study is to examine and compare the classification performance of support vector machine models generated by various core functions used to classify diabetes mellitus in acute coronary syndrome patients. The secondary aim_ is to optimize the parameters of the various kernel functions which are used for constructing the support vector machine model and to achieve the best classification performance.

Material and Method: The data examined in this study were selected retrospectively from_ the database developed for Inonu University Turgut Ozal Medical Center Cardiology Department. The study included_ type 2 diabetes mellitus and various demographic and clinical variables in acute coronary syndrome patients. The Support Vector Machine model was used to classify type 2 diabetes mellitus in acute coronary syndrome patients. The related models are constructed by ANOVA radial basis function, bessel, linear, Gaussian radial basis function, laplace, polynomial and sigmoid kernel functions.

Results: The best classification performance was obtained by Support Vector Machine model constructed by laplace kernel function based on the results of performance metrics. The accuracy, area under ROC curve, sensitivity and specificity metrics with 95% CI were calculated as; 0.9804 (0.9716 - 0.987), 0.9332 (0.9096 - 0.9567), 0.9999 (0.9791 – 1.000) and 0.9776 (0.9675 – 0.9852), respectively.

Conclusion: When the performance metrics were taken into account, the best classification performance was achieved from the Laplace Support Vector Machine model. In subsequent studies, the use of Support Vector Machine models with different kernel functions and other machine learning or data mining algorithms in different clinical trials may improve the classification success of the diseases.