Machine Learning Framework for Cardiovascular Disease Prediction Using SVM

Authors

  • Tayyaba Tabassum, Dr. Afroze Ansari, Syeda Faqera Fatima, Zameer Ahamad B

Keywords:

Cardiovascular disease, machine learning, risk prediction, logistic regression, support vector machine, AUC, recall, supervised learning

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, presenting a critical public health concern. Heart-related conditions, in particular, account for the majority of these deaths. Early and accurate prediction of CVD risk is therefore essential for timely intervention and effective management.

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References

https://www.who.int/news-room/fact sheets/detail/cardiovasculardiseases-(cvd)

Kelly, B. B., & Fuster, V. (Eds.). (2010). Promoting cardiovascular health in the developing world: a critical challenge to achieve global health. National Academies Press.

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Published

2023-03-29

How to Cite

Tayyaba Tabassum, Dr. Afroze Ansari, Syeda Faqera Fatima, Zameer Ahamad B. (2023). Machine Learning Framework for Cardiovascular Disease Prediction Using SVM . Pegem Journal of Education and Instruction, 13(3), 542–556. Retrieved from https://www.pegegog.net/index.php/pegegog/article/view/4145

Issue

Vol. 13 No. 3 (2023): PEGEGOG

Section

Article

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