Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review

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Published: Jun 9, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.7112
Keywords:
Adaboost, XG-boost, Naive Bayes, SVM, Decision Tree, Random Forest, Logistic Regression, confusion matrix, correlation matrix

Main Article Content

Danish Quamar
Department Of Computer Science & Information Technology, Maulana Azad National Urdu University, Hyderabad, India
Mohammad Islam
Assistant Professor, Department Of Computer Science & Information Technology, Maulana Azad National Urdu University, Hyderabad, India

Abstract

Accurate diagnosis and early detection of heart disease can help save lives because it is the primary cause of mortality. If a forecast is inaccurate, patients could potentially suffer significant harm. Today, it is challenging to predict and identify heart disease. 24 hour monitoring is not practical due to the extensive equipment and time required. Heart disease treatments can be both expensive and challenging. In order to obtain the data from databases and use this information to successfully forecast cardiac illness, a variety of data mining techniques and machine learning algorithms are now accessible. We have used every technique to put the heart disease prognosis into practise. The algorithms used in SVM, NAIVE BAYER, REGRESSION, KNN, ADABOOST, DECISION TREE, and XG-BOOST And Voting Ensemble Method.

Article Details

How to Cite
Quamar, D. ., & Islam, M. . (2023). Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 511–521. https://doi.org/10.17762/ijritcc.v11i5s.7112
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

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