نوع مقاله : مقاله پژوهشی
نویسندگان
گروه مهندسی کامپیوتر، واحد فومن و شفت، دانشگاه آزاد اسلامی، فومن، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The spreading of artificial intelligence, especially machine learning applications in the medical field, has opened that possibility of raising the precision and effectiveness of diagnosis and treatment of diseases. One of the important applications is the facilitation of the detection of the patient's response to the treatment by cardiac resynchronization therapy, a treatment method for patients with heart failure in which the function of the heart is improved by the coordinated stimulation of the ventricles. One of the main challenges in this area is class imbalance among the numbers of responders and non-responders to the treatment, which lowers the accuracy of the classification models. This research proposes a hybrid method based on Synthetic Minority Over-sampling with generalized class and ensemble learning. To improve the quality of synthetic samples, the Crow Search Algorithm has been used as a metaheuristic method and the Genetic Algorithm has been used for dimensionality reduction and efficient feature selection. For the classification stage, two ensemble learning models of Gradient Boosting and Random Forest were implemented. The utilized dataset includes 60 initial features, which have been reduced to 41 optimized selected features using the genetic algorithm. The criterion of optimality was the maximization of model accuracy in the identification of non-responders. The performance of the proposed method with these 41 selected features showed the average harmonic with the value of 89.07% and the accuracy of the non-response class of 93.59%. The results of this study indicated the combination of optimization, oversampling, and ensemble learning methods could effectively increase the identification accuracy of the non-responder patients of cardiac resynchronization therapy and thus provide assistive data-driven medical decision-making.
کلیدواژهها [English]
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