Efficient Predictive Modelling for Classification of Coronary Artery Diseases Using Machine Learning Approach Article Swipe
Savita Savita
,
Ganga Sharma
,
Geeta Rani
,
Vijaypal Singh Dhaka
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1757-899x/1099/1/012068
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1088/1757-899x/1099/1/012068
Cardiovascular is one of the most critical diseases that affect persons very abominably. Coronary artery diseases (CAD) are one of the categories of cardiovascular diseases that cause a high death rate. So it is imperative to control these death rates by developing an advanced model of machine learning through which diseases can be detected at the premature stage. Due to the lack of enough facilities for tools like angiography it has become a substantial challenge for the health care organization to detect such diseases. If tools exist, then these are known for being expensive and also have numerous side effects. The main goal of this research is to enhance the accuracy of existing models using optimization techniques with machine learning techniques. Alizadeh-Sani CAD dataset has been used which consists of a total of 303 records with 56 attributes. The proposed model reported following values of precision (0.92), recall (0.92), and accuracy (0.93). This proves the efficacy of employing the optimization techniques with machine learning algorithms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1757-899x/1099/1/012068
- https://iopscience.iop.org/article/10.1088/1757-899X/1099/1/012068/pdf
- OA Status
- diamond
- Cited By
- 4
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3136135302
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3136135302Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1757-899x/1099/1/012068Digital Object Identifier
- Title
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Efficient Predictive Modelling for Classification of Coronary Artery Diseases Using Machine Learning ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-03-01Full publication date if available
- Authors
-
Savita Savita, Ganga Sharma, Geeta Rani, Vijaypal Singh DhakaList of authors in order
- Landing page
-
https://doi.org/10.1088/1757-899x/1099/1/012068Publisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.1088/1757-899X/1099/1/012068/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
- OA URL
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https://iopscience.iop.org/article/10.1088/1757-899X/1099/1/012068/pdfDirect OA link when available
- Concepts
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CAD, Machine learning, Artificial intelligence, Computer science, Coronary artery disease, Coronary angiography, Precision and recall, Medicine, Cardiology, Myocardial infarction, Engineering, Engineering drawingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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19Number 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.most | 6 |
| abstract_inverted_index.side | 99 |
| abstract_inverted_index.such | 83 |
| abstract_inverted_index.that | 9, 26 |
| abstract_inverted_index.then | 88 |
| abstract_inverted_index.this | 105 |
| abstract_inverted_index.used | 127 |
| abstract_inverted_index.very | 12 |
| abstract_inverted_index.with | 118, 136, 162 |
| abstract_inverted_index.(CAD) | 17 |
| abstract_inverted_index.being | 93 |
| abstract_inverted_index.cause | 27 |
| abstract_inverted_index.death | 30, 39 |
| abstract_inverted_index.known | 91 |
| abstract_inverted_index.model | 45, 141 |
| abstract_inverted_index.rate. | 31 |
| abstract_inverted_index.rates | 40 |
| abstract_inverted_index.these | 38, 89 |
| abstract_inverted_index.tools | 67, 86 |
| abstract_inverted_index.total | 132 |
| abstract_inverted_index.using | 115 |
| abstract_inverted_index.which | 50, 128 |
| abstract_inverted_index.affect | 10 |
| abstract_inverted_index.artery | 15 |
| abstract_inverted_index.become | 72 |
| abstract_inverted_index.detect | 82 |
| abstract_inverted_index.enough | 64 |
| abstract_inverted_index.exist, | 87 |
| abstract_inverted_index.health | 78 |
| abstract_inverted_index.models | 114 |
| abstract_inverted_index.proves | 154 |
| abstract_inverted_index.recall | 148 |
| abstract_inverted_index.stage. | 58 |
| abstract_inverted_index.values | 144 |
| abstract_inverted_index.(0.92), | 147, 149 |
| abstract_inverted_index.(0.93). | 152 |
| abstract_inverted_index.control | 37 |
| abstract_inverted_index.dataset | 124 |
| abstract_inverted_index.enhance | 109 |
| abstract_inverted_index.machine | 47, 119, 163 |
| abstract_inverted_index.persons | 11 |
| abstract_inverted_index.records | 135 |
| abstract_inverted_index.through | 49 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Coronary | 14 |
| abstract_inverted_index.accuracy | 111, 151 |
| abstract_inverted_index.advanced | 44 |
| abstract_inverted_index.consists | 129 |
| abstract_inverted_index.critical | 7 |
| abstract_inverted_index.detected | 54 |
| abstract_inverted_index.diseases | 8, 16, 25, 51 |
| abstract_inverted_index.effects. | 100 |
| abstract_inverted_index.efficacy | 156 |
| abstract_inverted_index.existing | 113 |
| abstract_inverted_index.learning | 48, 120, 164 |
| abstract_inverted_index.numerous | 98 |
| abstract_inverted_index.proposed | 140 |
| abstract_inverted_index.reported | 142 |
| abstract_inverted_index.research | 106 |
| abstract_inverted_index.challenge | 75 |
| abstract_inverted_index.diseases. | 84 |
| abstract_inverted_index.employing | 158 |
| abstract_inverted_index.expensive | 94 |
| abstract_inverted_index.following | 143 |
| abstract_inverted_index.precision | 146 |
| abstract_inverted_index.premature | 57 |
| abstract_inverted_index.categories | 22 |
| abstract_inverted_index.developing | 42 |
| abstract_inverted_index.facilities | 65 |
| abstract_inverted_index.imperative | 35 |
| abstract_inverted_index.techniques | 117, 161 |
| abstract_inverted_index.abominably. | 13 |
| abstract_inverted_index.algorithms. | 165 |
| abstract_inverted_index.angiography | 69 |
| abstract_inverted_index.attributes. | 138 |
| abstract_inverted_index.substantial | 74 |
| abstract_inverted_index.techniques. | 121 |
| abstract_inverted_index.optimization | 116, 160 |
| abstract_inverted_index.organization | 80 |
| abstract_inverted_index.Alizadeh-Sani | 122 |
| abstract_inverted_index.Cardiovascular | 1 |
| abstract_inverted_index.cardiovascular | 24 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5057070686 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I73779912 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.6 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |