Makine öğrenimi yöntemleri ile kalp krizinin sınıflandırılması ve ilişkili risk faktörlerinin belirlenmesi için bir model oluşturulması

Abstract

Object: Increased survival rates in heart attacks (HAs) depend on early intervention and treatment. In this study, it is aimed to predict the factors that may be associated with HA and to determine which factor is more effective by using Stochastic Gradient Boosting (SGB) method, one of the machine learning methods. Methods: An open access data set was used in the study. The 5-fold cross-validation method was used in modeling and the data set was divided into training and test data sets as 80%:20%. Accuracy (ACC), balanced accuracy (b-ACC), sensitivity (SE), specificity (SP), positive predictive value (ppv), negative predictive value (npv) and F1 score metrics were used for model evaluation. Results: The results obtained from the performance metrics with the modeling were 98.9%, 98.7%, 99.4%, 98.0%, 98.8%, 99%, and 99.1% for ACC, b-ACC, SE, SP, ppv, npv, and F1-score, respectively. According to variable importance values, troponin and CK-MB appear to be associated with HA, respectively. Conclusion: According to the modeling results, factors that may be associated with heart attack were determined with high accuracy by machine learning method. Thanks to these two enzymes, early diagnosis can be made in individuals at risk of having a heart attack, and poor prognosis and deaths can be prevented.