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Heart disease is a deadly disease that large population of people around the world suffers from. When considering death rates and large number of people who suffers from heart disease, it is revealed how important early diagnosis of heart disease. Traditional way of diagnosis is not sufficient for such an illness. Developing a medical diagnosis system based on machine learning for prediction of heart disease provides more accurate diagnosis than traditional way. In this, a heart disease prediction system which uses SVM algorithm is proposed. 13 clinical features were used as input for the SVM and then the SVM was trained to predict absence or presence of heart disease with accuracy of 95%.
Keywords:
SVM, Heart Diseases Prediction System, Datamining
Cite Article:
"A Review on Human Heart Prediction System Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.4, Issue 3, page no.88 - 92, March-2019, Available :http://www.ijrti.org/papers/IJRTI1903021.pdf
Downloads:
000204773
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