Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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)
Sudden cardiac arrest is one of the many cardiovascular disorders that is on the rise. There are a variety of factors that affect our hearts, and changes in our everyday routines may also contribute to cardiovascular disease. If health measures are not taken, a person's sudden cardiac arrest might result in death. We will use machine learning to forecast the likelihood of a person developing a cardiac risk. Using a machine learning technique, this module calculates the chances of being diagnosed with this condition based on several variables.
Keywords:
Cardiac Risk, Support Vector Machine (SVM), Decision Tree, Random Forest, Bagging.
Cite Article:
"CARDIAC RISK PREDICTION", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 5, page no.794 - 798, May-2022, Available :http://www.ijrti.org/papers/IJRTI2205130.pdf
Downloads:
000205089
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