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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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Volume 9 | Issue 5
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| Paper Title: | Heart Attack Prediction Using Data Science And Machine Learning |
| Authors Name: | Nikunj Aggarwal , Raghav Gaur , Yug Varshney , Divyansh Rastogi , Mayank Mahajan |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_209363
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| Published Paper Id: | IJNRD2401192 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | This study has been undertaken to study Cardiovascular conditions, particularly heart attacks, remain a leading cause of global morbidity and mortality. Beforehand vaticination of individualities at threat can significantly ameliorate preventative interventions and patient issues. This exploration paper presents a comprehensive study on the integration of data wisdom and machine literacy ways for heart attack vaticination. The study employs a different dataset encompassing demographic information, life factors, and clinical pointers, collected from a large cohort of cases. Data preprocessing ways are applied to address missing values, and outliers, and ensure the quality of the dataset. point engineering is conducted to prize applicable information, and a relative analysis of colorful point selection styles is performed to identify the most influential predictors. Several machines learning algorithms, including but not limited to logistic retrogression, support vector machines, arbitrary timbers, and deep neural networks, are employed to develop prophetic models. The models are trained and validated using a robust cross-validation strategy to insure generalizability. Performance criteria similar as delicacy, perceptivity |
| Keywords: | Heart attack, Decision Tree,Machine Learning ,Random Forest |
| Cite Article: | "Heart Attack Prediction Using Data Science And Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.b811-b815, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401192.pdf |
| Downloads: | 000118751 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2401192 Registration ID: 209363 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: b811-b815 Country: Mohali, Punjab, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401192 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401192 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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