این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Industrial and Systems Engineering، جلد ۱۴، شماره Special issue: ۱۸th International Industrial Engineering Conference، صفحات ۴۰-۴۷
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Predicting coronary artery diseases using effective features selected by Harris Hawks optimization algorithm and support vector machine
چکیده انگلیسی مقاله With 17 million annual deaths, cardiovascular diseases are the leading cause of mortality across the world with coronary artery disease (CAD) as the most prevalent one. CAD is the leading cause of death in industrial countries and at the same time is rapidly spreading in the developing world. Thus, the development and introduction of machine learning methods for the accurate diagnosis of heart diseases, especially CAD, have been an important debate in recent years in order to overcome relevant problems. The aim of this paper was to propose a model for enhancing CAD prediction accuracy. It sought a framework for predicting and diagnosing CAD using the features selection of Harris Hawks Optimization algorithm (HHO) and Support Vector Machine (SVM). The heart disease data set of Cleveland hospital available in the University of California Irvine (UCI) was used as the studied data set. It included 303 cases. Each case had 14 features with the final medical status of cases (CAD or normal case) as one of the features where 165 and 138 cases were diagnosed as CAD and normal, respectively. The results of this study revealed that HHO could enhance CAD diagnosis accuracy.
کلیدواژه‌های انگلیسی مقاله CORONARY ARTERY DISEASES,Feature selection,Harris Hawk optimization algorithm,Support Vector Machine
نویسندگان مقاله Sarina Maleki |
Department of Industrial Engineering, Technical Engineering Faculty, Yazd University, Yazd, Iran

Yahia Zare Mehrjerdi |
Department of Industrial Engineering, Technical Engineering Faculty, Yazd University, Yazd, Iran

Davood Shishebori |
Department of Industrial Engineering, Technical Engineering Faculty, Yazd University, Yazd, Iran

Masoud Mirzaei |
Disease Modeling Center of Shahid Sadoughi University of Medical Sciences, Yazd, Iran

نشانی اینترنتی https://www.jise.ir/article_143910_2f554e7b25ddfcfdf68e4cee065983f1.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات