استراتژی الهام گرفته از فیزیک درحل مسائل دشوار
  •   آرشیو : نسخه پاییز 1398
  •   نوع مقاله : پژوهشی
  •   کد پذیرش : 1348
  •   موضوع : سایر شاخه های علوم رایانه
  • نویسنده/گان : امیراحمد نیری
  •   کلید واژه : داده کاوی، شبکه ‏های عصبی، تشخیص آریتمی قلبی، ترکیب خبره ها
  •   Abstract :
    Heart disease is one of the most common types of disease With high rates of mortality. Arrhythmias are abnormal beats, which make the heart work too fast (tachycardia) or very slowly (bradycardia). The purpose of this research is to provide a different model based on the data mining technique. Which has the ability to normal rate and five abnormal rate of the heart. The present study was a diagnostic study conducted on UCI datasets includes 452 samples and 279 features. To determine the type of arrhythmia, all samples are classified into five general categories. In this study the combination of neural networks in a hierarchical way (mixture of experts) was produced. In all networks, 70% of the samples were used for training and 30% were used for testing. After modeling and comparing the produced models, the accuracy of the prediction of arrhythmia was 89.5% when using a neural network and 93.5% of the hierarchical order of the experts. one of the researchers' goals is Reducing the diagnostic error of heart arrhythmia. Using data mining methods can help reduce this error. This study, with the help of data mining, diagnosis of cardiac arrhythmia, suggests that providing a more robust method of combining neural networks in a hierarchical manner leads to improved diagnostic accuracy.
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