Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction Article Swipe
Sulagna Mohapatra
,
Prasan Kumar Sahoo
,
Suvendu Kumar Mohapatra
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13010163
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13010163
The generation of a huge volume of structured, semi-structured and unstructured real-time health monitoring data and its storage in the form of electronic health records (EHRs) need to be processed and analyzed intelligently to provide timely healthcare. A big data analytic platform is an alternative to the traditional warehouse paradigms for the processing, analysis and storage of the tremendous volume of healthcare data. However, the manual analysis of these voluminous, multi-variate patients data is tedious and error-prone. Therefore, an intelligent solution method is highly essential to perform multiple correlation analyses for disease diagnosis and prediction. In this paper, first, a structural framework is proposed to process the huge volume of cardiological big data generated from the hospital and patients. Then, an intelligent analytical model for the cardiological big data analysis is proposed by combining the concept of artificial neural network (ANN) and particle swarm optimization (PSO) to predict the abnormalities in the cardiac health of a person. In the proposed cardiac disease prediction model, an extensive electrocardiogram (ECG) data analysis method is developed to identify the probable normal and abnormal cardiac feature points. Simulation results show the effects of a number of attributes for improving the accuracy of the cardiac disease prediction and data processing time in the cloud with an increase in the number of the cardiac patients.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13010163
- https://www.mdpi.com/2079-9292/13/1/163/pdf?version=1703946692
- OA Status
- gold
- Cited By
- 12
- References
- 49
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4390437214Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics13010163Digital Object Identifier
- Title
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Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease PredictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-29Full publication date if available
- Authors
-
Sulagna Mohapatra, Prasan Kumar Sahoo, Suvendu Kumar MohapatraList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics13010163Publisher landing page
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https://www.mdpi.com/2079-9292/13/1/163/pdf?version=1703946692Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-9292/13/1/163/pdf?version=1703946692Direct OA link when available
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Big data, Artificial neural network, Computer science, Particle swarm optimization, Volume (thermodynamics), Data mining, Artificial intelligence, Process (computing), Machine learning, Operating system, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 4Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.health | 12, 23, 153 |
| abstract_inverted_index.highly | 83 |
| abstract_inverted_index.manual | 65 |
| abstract_inverted_index.method | 81, 170 |
| abstract_inverted_index.model, | 163 |
| abstract_inverted_index.neural | 138 |
| abstract_inverted_index.normal | 177 |
| abstract_inverted_index.number | 190, 214 |
| abstract_inverted_index.paper, | 97 |
| abstract_inverted_index.timely | 35 |
| abstract_inverted_index.volume | 5, 59, 108 |
| abstract_inverted_index.cardiac | 152, 160, 180, 199, 217 |
| abstract_inverted_index.concept | 135 |
| abstract_inverted_index.disease | 91, 161, 200 |
| abstract_inverted_index.effects | 187 |
| abstract_inverted_index.feature | 181 |
| abstract_inverted_index.network | 139 |
| abstract_inverted_index.perform | 86 |
| abstract_inverted_index.person. | 156 |
| abstract_inverted_index.points. | 182 |
| abstract_inverted_index.predict | 147 |
| abstract_inverted_index.process | 105 |
| abstract_inverted_index.provide | 34 |
| abstract_inverted_index.records | 24 |
| abstract_inverted_index.results | 184 |
| abstract_inverted_index.storage | 17, 55 |
| abstract_inverted_index.tedious | 74 |
| abstract_inverted_index.However, | 63 |
| abstract_inverted_index.abnormal | 179 |
| abstract_inverted_index.accuracy | 196 |
| abstract_inverted_index.analyses | 89 |
| abstract_inverted_index.analysis | 53, 66, 129, 169 |
| abstract_inverted_index.analytic | 40 |
| abstract_inverted_index.analyzed | 31 |
| abstract_inverted_index.hospital | 116 |
| abstract_inverted_index.identify | 174 |
| abstract_inverted_index.increase | 211 |
| abstract_inverted_index.multiple | 87 |
| abstract_inverted_index.particle | 142 |
| abstract_inverted_index.patients | 71 |
| abstract_inverted_index.platform | 41 |
| abstract_inverted_index.probable | 176 |
| abstract_inverted_index.proposed | 103, 131, 159 |
| abstract_inverted_index.solution | 80 |
| abstract_inverted_index.combining | 133 |
| abstract_inverted_index.developed | 172 |
| abstract_inverted_index.diagnosis | 92 |
| abstract_inverted_index.essential | 84 |
| abstract_inverted_index.extensive | 165 |
| abstract_inverted_index.framework | 101 |
| abstract_inverted_index.generated | 113 |
| abstract_inverted_index.improving | 194 |
| abstract_inverted_index.paradigms | 49 |
| abstract_inverted_index.patients. | 118, 218 |
| abstract_inverted_index.processed | 29 |
| abstract_inverted_index.real-time | 11 |
| abstract_inverted_index.warehouse | 48 |
| abstract_inverted_index.Simulation | 183 |
| abstract_inverted_index.Therefore, | 77 |
| abstract_inverted_index.analytical | 122 |
| abstract_inverted_index.artificial | 137 |
| abstract_inverted_index.attributes | 192 |
| abstract_inverted_index.electronic | 22 |
| abstract_inverted_index.generation | 1 |
| abstract_inverted_index.healthcare | 61 |
| abstract_inverted_index.monitoring | 13 |
| abstract_inverted_index.prediction | 162, 201 |
| abstract_inverted_index.processing | 204 |
| abstract_inverted_index.structural | 100 |
| abstract_inverted_index.tremendous | 58 |
| abstract_inverted_index.alternative | 44 |
| abstract_inverted_index.correlation | 88 |
| abstract_inverted_index.healthcare. | 36 |
| abstract_inverted_index.intelligent | 79, 121 |
| abstract_inverted_index.prediction. | 94 |
| abstract_inverted_index.processing, | 52 |
| abstract_inverted_index.structured, | 7 |
| abstract_inverted_index.traditional | 47 |
| abstract_inverted_index.voluminous, | 69 |
| abstract_inverted_index.error-prone. | 76 |
| abstract_inverted_index.optimization | 144 |
| abstract_inverted_index.unstructured | 10 |
| abstract_inverted_index.abnormalities | 149 |
| abstract_inverted_index.cardiological | 110, 126 |
| abstract_inverted_index.intelligently | 32 |
| abstract_inverted_index.multi-variate | 70 |
| abstract_inverted_index.semi-structured | 8 |
| abstract_inverted_index.electrocardiogram | 166 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5043838453 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I173093425, https://openalex.org/I3020100970 |
| citation_normalized_percentile.value | 0.95714764 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |