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)
The medical sector processes vast amounts of data on a regular basis. Handling large data in the old way can affect the results. Advanced data mining techniques are especially used in heart disease prediction to find facts about databases and medical research. Heart disease is the world's largest cause of death. The tremendous amount of data generated for the prediction of heart disease is too difficult and wasteful to process and analyze in the conventional way. Data mining provides methodologies and techniques to transform these mounds into useful information for decision-making. Using data mining algorithms, you can quickly predict disease with high accuracy. In this paper, a single or hybrid combination of data mining algorithms can be used to investigate several papers used in cardiac disease prediction to identify algorithms for future research with high accuracy.
Keywords:
Data mining; Heart disease prediction; Data mining techniques.
Cite Article:
"A Literature Review on Heart Disease Prediction Based on Data Mining Algorithms", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 6, page no.100 - 102, June-2018, Available :http://www.ijrti.org/papers/IJRTI180238.pdf
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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