A New-fangled Classification Algorithm for Medical Heart Diseases Analysis using Wavelet Transforms Article Swipe
Soumya Ranjan Nayak
,
S. Sivakumar
,
B. Sripathy
,
Prabhishek Singh
,
Manoj Diwakar
,
Indrajeet Gupta
,
Vinayakumar Ravi
,
Alanoud Al Mazroa
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2174/0118749445343675241022102900
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2174/0118749445343675241022102900
Background In this article, the Mixed Mode Database Miner (MMDBM) algorithm is introduced for the classification of data. This algorithm depends on the decision tree classifier, which handles the numerical and categorical attributes. For the experimental analysis in a well-explored heart disease data set collected from the UCI Repository. Aims Understanding the fundamentals of every classification method and how to apply them to CUDA is the aim of this study. After reviewing the literature, the approach that is most suited is selected for implementing the suggested algorithm in the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification. Objective The main objective of the manuscript is to identify the early stage of heart attack that was caused either by smoking, smoking with tobacco, or non-smoking. Additionally, this study aims to check the validity of the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification. Methods In the modern digital world today, data has a major impact on everyone’s life. Every database contains a lot of information hidden either in the form of numerical data, characteristic data, or a mixture of both. Moreover, to accurately decode every dataset, a fast and efficient classifier is essential.. Moreover, by using this hybrid technique based on both MMDBM and Wavelet processes, it compresses the data with minimum storage capacity. Results The experimental results are compared with the classified data to wavelet data output. These results prove that our presented technique is more prominent and robust in an analysis of heart diseases. Conclusion This algorithm is based on a decision tree classifier and tested on a heart disease database. MMDBM is applied to classify a large data set of numerical and categorical attributes. The data are compressed with the help of a one-dimensional wavelet transform. This is one of the new approaches applied to classified data in real-time applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2174/0118749445343675241022102900
- OA Status
- diamond
- Cited By
- 1
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405088578
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405088578Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2174/0118749445343675241022102900Digital Object Identifier
- Title
-
A New-fangled Classification Algorithm for Medical Heart Diseases Analysis using Wavelet TransformsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-07Full publication date if available
- Authors
-
Soumya Ranjan Nayak, S. Sivakumar, B. Sripathy, Prabhishek Singh, Manoj Diwakar, Indrajeet Gupta, Vinayakumar Ravi, Alanoud Al MazroaList of authors in order
- Landing page
-
https://doi.org/10.2174/0118749445343675241022102900Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2174/0118749445343675241022102900Direct OA link when available
- Concepts
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Wavelet, Computer science, Artificial intelligence, Pattern recognition (psychology), Algorithm, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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22Number 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.identify | 132 |
| abstract_inverted_index.observed | 102, 173 |
| abstract_inverted_index.provides | 114, 185 |
| abstract_inverted_index.selected | 81 |
| abstract_inverted_index.smoking, | 144 |
| abstract_inverted_index.tobacco, | 147 |
| abstract_inverted_index.validity | 157 |
| abstract_inverted_index.Moreover, | 231, 244 |
| abstract_inverted_index.Objective | 123 |
| abstract_inverted_index.algorithm | 10, 19, 86, 113, 184, 299 |
| abstract_inverted_index.analysis, | 100, 171 |
| abstract_inverted_index.capacity. | 264 |
| abstract_inverted_index.collected | 44 |
| abstract_inverted_index.database. | 313 |
| abstract_inverted_index.diseases. | 296 |
| abstract_inverted_index.efficient | 240 |
| abstract_inverted_index.numerical | 29, 222, 324 |
| abstract_inverted_index.objective | 126 |
| abstract_inverted_index.presented | 284 |
| abstract_inverted_index.prominent | 288 |
| abstract_inverted_index.real-time | 352 |
| abstract_inverted_index.reviewing | 71 |
| abstract_inverted_index.suggested | 85 |
| abstract_inverted_index.technique | 109, 180, 249, 285 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 297 |
| abstract_inverted_index.accurately | 233 |
| abstract_inverted_index.approaches | 346 |
| abstract_inverted_index.classified | 273, 349 |
| abstract_inverted_index.classifier | 90, 161, 241, 306 |
| abstract_inverted_index.compressed | 331 |
| abstract_inverted_index.compresses | 258 |
| abstract_inverted_index.introduced | 12 |
| abstract_inverted_index.manuscript | 129 |
| abstract_inverted_index.processes, | 256 |
| abstract_inverted_index.transform. | 339 |
| abstract_inverted_index.Repository. | 48 |
| abstract_inverted_index.attributes. | 32, 327 |
| abstract_inverted_index.categorical | 31, 326 |
| abstract_inverted_index.classifier, | 25 |
| abstract_inverted_index.essential.. | 243 |
| abstract_inverted_index.information | 215 |
| abstract_inverted_index.literature, | 73 |
| abstract_inverted_index.everyone’s | 207 |
| abstract_inverted_index.experimental | 35, 99, 170, 267 |
| abstract_inverted_index.fundamentals | 52 |
| abstract_inverted_index.implementing | 83 |
| abstract_inverted_index.non-smoking. | 149 |
| abstract_inverted_index.Additionally, | 150 |
| abstract_inverted_index.Understanding | 50 |
| abstract_inverted_index.applications. | 353 |
| abstract_inverted_index.well-explored | 39 |
| abstract_inverted_index.characteristic | 224 |
| abstract_inverted_index.classification | 15, 55 |
| abstract_inverted_index.decomposition. | 96, 167 |
| abstract_inverted_index.classification. | 122, 193 |
| abstract_inverted_index.one-dimensional | 337 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.83079169 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |