چكيده به لاتين
Diagnosis and determination of appropriate treatment for patients are crucial in medical science. Cardiovascular surgery is one of the most common surgeries performed to increase survival and improve the quality of life in patients. Mortality prediction plays a vital role in patients undergoing heart surgery. Heart surgery departments of hospitals are producing remarkable recorded data during a day, which should be utilized by data scientists to quantify the patient's health and foresee future incidents. Diagnosis and determination of appropriate treatment for patients are crucial in medical science. data analysis uses machine learning in the field of disease, provides good opportunities to explore the hidden patterns of this collection. An exact forecast of the mortality after heart surgery will cause Successful medical treatment and fewer costs. This study was performed using the data of 1933 patients who underwent various heart surgeries (from 2009 to 2015 in Mashhad University of Medical Sciences and Shahid Beheshti University of Tehran), based on a stacking model and majority voting. In addition, in this research, feature selection methods have been used, which are: Mutual information method, Feature elimination recursive, Randomforest, Decision tree and 11 features that were more common, such as EF and HTN, were selected as the most important features. The grid search method is used to find the appropriate parameter in each data analyzing model. It should be noted that the stacking model and majority voting are compared with different data mining models, including random forest, decision tree, logistic regression, nearest neighbor, artificial neural network, gradient boosting, and bagging. The results show that the final hybrid model in this study has the best performance in predicting patients' life status with the area under the characteristic ROC curve 0.81, sensitivity, 0.62, accuracy, 0.96, and F1, 0.76, among other implemented models.
