Enhancing the Diagnosis of Cardiovascular Disease: A Comparative Examination of Support Vector Machine and Artificial Neural Network Models Utilizing Extensive Data Preprocessing Techniques Article Swipe
Ankur Kumar
,
Asim Ali Khan
,
Jaspreet Singh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37394/23205.2024.23.31
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37394/23205.2024.23.31
This research delves into the classification of cardiovascular disease (CVD) utilizing state-of-the-art machine learning algorithms, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). Before model training, extensive data preprocessing techniques were implemented, including data cleaning, feature scaling, encoding, Feature selection, handling imbalanced data, normalization, and cross-validation. After data preparation, an extensive evaluation of performance was carried out against various parameters like accuracy, precision, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odd ratio (DOR). The comparison of SVM and ANN techniques indicates that the SVM has a better sensitivity in detecting positive cases while ANNs have more accuracy in the classification. This paper not only documents the use of new methods but also highlights the advantages and disadvantages of SVM and ANN models, and therefore helps to improve the use of machine learning applications in making health care decisions on CVD diagnosis.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.37394/23205.2024.23.31
- OA Status
- diamond
- Cited By
- 7
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407772486
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407772486Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.37394/23205.2024.23.31Digital Object Identifier
- Title
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Enhancing the Diagnosis of Cardiovascular Disease: A Comparative Examination of Support Vector Machine and Artificial Neural Network Models Utilizing Extensive Data Preprocessing TechniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-31Full publication date if available
- Authors
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Ankur Kumar, Asim Ali Khan, Jaspreet SinghList of authors in order
- Landing page
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https://doi.org/10.37394/23205.2024.23.31Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.37394/23205.2024.23.31Direct OA link when available
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Support vector machine, Artificial neural network, Preprocessor, Computer science, Artificial intelligence, Machine learning, Data pre-processing, Data mining, Pattern recognition (psychology)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
- References (count)
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17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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