Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification Article Swipe
Ashir Javeed
,
Sanam Shahla Rizvi
,
Shijie Zhou
,
Rabia Riaz
,
Shafqat Ullah Khan
,
Se Jin Kwon
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/8843115
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/8843115
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2020/8843115
- https://downloads.hindawi.com/journals/misy/2020/8843115.pdf
- OA Status
- hybrid
- Cited By
- 70
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3080774167
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- OpenAlex ID
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https://openalex.org/W3080774167Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2020/8843115Digital Object Identifier
- Title
-
Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-26Full publication date if available
- Authors
-
Ashir Javeed, Sanam Shahla Rizvi, Shijie Zhou, Rabia Riaz, Shafqat Ullah Khan, Se Jin KwonList of authors in order
- Landing page
-
https://doi.org/10.1155/2020/8843115Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/misy/2020/8843115.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/misy/2020/8843115.pdfDirect OA link when available
- Concepts
-
Computer science, Feature selection, Artificial neural network, Artificial intelligence, Receiver operating characteristic, Machine learning, Feature (linguistics), Heart disease, Feature engineering, Data mining, Pattern recognition (psychology), Deep learning, Medicine, Linguistics, Cardiology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
70Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 11, 2023: 21, 2022: 27, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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