Heart Risk Prediction using Machine Learning : A Literature Review

Authors

  • Om Deshmukh  Computer Engineering Department, Student, Shree L.R. Tiwari College of Engineering, Mira Road, Thane, Maharashtra, India
  • Fardeen Kachawa  Computer Engineering Department, Student, Shree L.R. Tiwari College of Engineering, Mira Road, Thane, Maharashtra, India
  • Sujal Bhatt  Computer Engineering Department, Student, Shree L.R. Tiwari College of Engineering, Mira Road, Thane, Maharashtra, India
  • Kaif Siddique  Computer Engineering Department, Student, Shree L.R. Tiwari College of Engineering, Mira Road, Thane, Maharashtra, India
  • Bhavesh Choudhary  Computer Engineering Department, Student, Shree L.R. Tiwari College of Engineering, Mira Road, Thane, Maharashtra, India
  • Neelam Phadnis  Computer Engineering Department, Assistant Professor, Shree L.R. Tiwari College of Engineering, Mira Road, Thane, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT2390439

Keywords:

Machine Learning, Logistic Regression, Naive Bayes, SVM, Classification Algorithm, Data Mining

Abstract

Heart diseases are a leading cause of death among people compared to other diseases. The severity of these diseases has risen significantly in the past few years which has led to the rise of many researchers to present their work in the field of heart risk detection. Machine learning plays an important role in this with the most common machine learning algorithms used for this purpose being Logistic Regression, Naive Bayes, SVM, etc. All these algorithms fall under the classification algorithm category. Data mining plays an important role for feature selection from the dataset. The machine learning algorithms reviewed make use of the same UCI Cleveland dataset.

References

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Published

2023-08-30

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Section

Research Articles

How to Cite

[1]
Om Deshmukh, Fardeen Kachawa, Sujal Bhatt, Kaif Siddique, Bhavesh Choudhary, Neelam Phadnis, " Heart Risk Prediction using Machine Learning : A Literature Review" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.409-413, July-August-2023. Available at doi : https://doi.org/10.32628/CSEIT2390439