Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification
Joint Authors
Khan, Shafqat Ullah
Riaz, Rabia
Kwon, Se Jin
Javeed, Ashir
Rizvi, Sanam Shahla
Zhou, Shijie
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-26
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Telecommunications Engineering
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192523