Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions Article Swipe
Ashir Javeed
,
Shafqat Ullah Khan
,
Liaqat Ali
,
Sardar Ali
,
Yakubu Imrana
,
Atiqur Rahman
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/9288452
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/9288452
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/9288452
- https://downloads.hindawi.com/journals/cmmm/2022/9288452.pdf
- OA Status
- hybrid
- Cited By
- 67
- References
- 144
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4212964221
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- OpenAlex ID
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https://openalex.org/W4212964221Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/9288452Digital Object Identifier
- Title
-
Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future DirectionsWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-03Full publication date if available
- Authors
-
Ashir Javeed, Shafqat Ullah Khan, Liaqat Ali, Sardar Ali, Yakubu Imrana, Atiqur RahmanList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/9288452Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/cmmm/2022/9288452.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/cmmm/2022/9288452.pdfDirect OA link when available
- Concepts
-
Modalities, Modality (human–computer interaction), Medical diagnosis, Heart failure, Machine learning, Heart disease, Artificial intelligence, Computer science, Feature (linguistics), Coronary artery disease, Medicine, Cardiology, Radiology, Sociology, Philosophy, Social science, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
67Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 13, 2023: 36, 2022: 12Per-year citation counts (last 5 years)
- References (count)
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144Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.modalities | 103 |
| abstract_inverted_index.prediction | 137 |
| abstract_inverted_index.procedures | 47 |
| abstract_inverted_index.systematic | 116, 129 |
| abstract_inverted_index.therefore, | 127 |
| abstract_inverted_index.Traditional | 45 |
| abstract_inverted_index.affordable, | 87 |
| abstract_inverted_index.limitations | 163 |
| abstract_inverted_index.modalities, | 143 |
| abstract_inverted_index.modalities. | 191 |
| abstract_inverted_index.researchers | 65 |
| abstract_inverted_index.responsible | 19 |
| abstract_inverted_index.techniques. | 80 |
| abstract_inverted_index.(angiography) | 48 |
| abstract_inverted_index.feature-based | 146 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5000478201 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I150229711, https://openalex.org/I83291730 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99041141 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |