چکیده مقاله
Cardiovascular disease, a leading cause of global mortality, requires effective identification of key risk factors for accurate diagnosis This study introduces a novel dynamic ensemble method, GMM based Dynamic Ensemble Learning Optimization, utilizing a Multi Objective Evolutionary Algorithm based on Decomposition, specifically designed for imbalanced datasets By integrating an evolutionary resampling technique inspired by the Gaussian mixture model GMM , the proposed approach addresses diversity, classifier performance, and the number of classifiers to generate optimal datasets The method is evaluated on six heart datasets, exhibiting varying imbalance rates, and compared against established ensemble learning algorithms, consistently demonstrating superior performance across multiple evaluation metrics Statistical significance tests, including McNemar's and Wilcoxon tests, further validate the findings Furthermore, the application of Survival Analysis using Kaplan Meier Estimates on a heart failure dataset enhances our understanding of heart disease prognosis In conclusion, this research contributes to accurate heart disease prediction and prognosis assessment by leveraging innovative data mining and ensemble learning techniques
کلیدواژهها
نویسندگان
Fatemeh Yazdi
Data Mining Laboratory, Industrial Engineering Department, Faculty of Engineering, College of Farabi, Universityof Tehran, Tehran, Iran
Shahrokh Asadi
Data Mining Laboratory, Industrial Engineering Department, Faculty of Engineering, College of Farabi, Universityof Tehran, Tehran, Iran
شیوه ارجاع
Yazdi, Fatemeh and Asadi, Shahrokh,1402,Optimizing Ensemble Learning for Accurate Identification and Prognostic Evaluation of Cardiovascular Disease,The 9th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
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