Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification
المؤلفون المشاركون
Khan, Shafqat Ullah
Riaz, Rabia
Kwon, Se Jin
Javeed, Ashir
Rizvi, Sanam Shahla
Zhou, Shijie
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-26
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis.
However, low heart disease prediction accuracy is still a problem in these systems.
For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE).
After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN).
Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN.
Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database.
The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve.
Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%.
Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis.
Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Javeed, Ashir& Rizvi, Sanam Shahla& Zhou, Shijie& Riaz, Rabia& Khan, Shafqat Ullah& Kwon, Se Jin. 2020. Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1192523
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Javeed, Ashir…[et al.]. Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification. Mobile Information Systems No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1192523
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Javeed, Ashir& Rizvi, Sanam Shahla& Zhou, Shijie& Riaz, Rabia& Khan, Shafqat Ullah& Kwon, Se Jin. Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1192523
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1192523
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر