DATA MINING APPROACH FOR IN-HOSPITAL TREATMENT OUTCOME IN PATIENTS WITH ACUTE CORONARY SYNDROME PROCENA INTRAHOSPITALNOG ISHODA LEČENJA PACIJENATA SA AKUTNIM KORONARNIM SINDROMOM METODOM ISTRAŽIVANJA PODATAKA

Summary Introduction. Risk stratification is nowadays crucial when esti- mating the patient's prognosis in terms of treatment outcome and it also helps in clinical decision making. Several risk assessment models have been developed to predict short-term outcomes in patients with acute coronary syndrome. This study was aimed at developing an outcome prediction model for patients with acute coronary syndrome submitted to percutaneus coronary interven- tion using data mining approach. Material and Methods. A total of 2030 patients hospitalized for acute coronary syndrome and treated with percutaneous coronary intervention from December 2008 to December 2011 were assigned to a derivation cohort. Demographic and anamnestic data, clinical characteristics on admission, biochemical analysis of blood parameters on admis- sion, and left ventricular ejection fraction formed the basis of the study. A number of machine learning algorithms available within Waikato Environment for Knowledge Discovery had been evalu- ated and the most successful was chosen. The predictive model was subsequently validated in a different population of 931 pa- tients (validation cohort), hospitalized during 2012. Results. The best prediction results were achieved using Alternating Decision Tree classifier, which was able to predict in-hospital mortality with 89% accuracy, and preserved good performance on valida- tion cohort with 87% accuracy. Alternating Decision Tree classi- fier identified a subset of 6 attributes most relevant to mortality prediction: systolic and diastolic blood pressure, heart rate, left ventricular ejection fraction, age, and troponin value. Conclusion. Data mining approach enabled the authors to develop a model capable of predicting the in-hospital outcome following percuta- neous coronary intervention. The model showed excellent sensitiv-