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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19446
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
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| Paper Title: | Machine Learning Approaches for Predicting and Preventing Adverse Cardiovascular Events |
| Authors Name: | Samreen Rizvi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_188475
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| Published Paper Id: | IJRTI2311042 |
| Published In: | Volume 8 Issue 11, November-2023 |
| DOI: | https://doi.org/10.5281/zenodo.10428214 |
| Abstract: | Over the last 10 years, a significant surge in cardiovascular diseases has been observed around the world. Considering the cruciality of the disease leads to rapid action toward the development of accurate cardiovascular disease risk prediction. However, currently, existing methods often fail to predict cardiovascular disease risk to diagnose patients that could have benefited via preventive treatment, while in other cases patients go through dispensable interceding. Machine learning techniques offer for cardiovascular disease prediction not only detect disease risk with maximum accuracy and precision but also exploit complex interactions for better disease prognosis. Effective and timely prediction of cardiovascular disease using patients’ health records not only assists in rapid diagnosis but also reduces mortality rate. In this article, we present a detailed comparative analysis of existing machine-learning techniques for cardiovascular disease prediction and prevention. Our research shows extensive analysis of around 35 papers related to machine learning-based cardiovascular disease prediction. This study will not only summarize the existing up-to-date approaches but also assist doctors in predicting heart disease risks |
| Keywords: | Artificial intelligence, machine learning, Cardiovascular Diseases, Heart Disease Risk Prediction |
| Cite Article: | "Machine Learning Approaches for Predicting and Preventing Adverse Cardiovascular Events", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 11, page no.289 - 293, November-2023, Available :http://www.ijrti.org/papers/IJRTI2311042.pdf |
| Downloads: | 000205080 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2311042
Registration ID:188475
Published In: Volume 8 Issue 11, November-2023
DOI (Digital Object Identifier): https://doi.org/10.5281/zenodo.10428214
Page No: 289 - 293 Country: Lucknow, UP, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2311042 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2311042 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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