A RELATIVE STUDY OF DIFFERENT MACHINE LEARNING CLASSIFICATION ALGORITHMS TO FORECAST THE HEART DISEASE

Authors

  • Bushra Memon SZABIST Hyderabad Campus
  • Sumbul Ghulamani

Abstract

Cardiovascular is comprehended as heart disease, and it covers different cases that impact the heart and have been the direct cause of death. It allies numerous risk elements in heart disease and the necessity to build practical strategies for earlier diagnosis to manage the condition promptly. The machine learning classifier has evolved significantly in the medical database, particularly for diagnosing disease. Nowadays, numerous organizations utilize these machine learning strategies to improve medical diagnostics for the earlier prognosis of conditions. This paper summarizes these machine learning algorithms in disease prediction and computational. Classification algorithms considered are logic regression, Naive Bayes, K-NN (nearest neighbor), (Support vector machine), SVM, (Decision Tree) DT, K-means clustering, and RF (random forest). This work reviewed 20 papers from 2018- 2021 that employed Classifier to detect specifically heart diseases in the medical sector during the last four years.

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Published

2022-05-30

How to Cite

Memon, B. ., & Ghulamani, S. . (2022). A RELATIVE STUDY OF DIFFERENT MACHINE LEARNING CLASSIFICATION ALGORITHMS TO FORECAST THE HEART DISEASE. Journal of Information Systems and Digital Technologies, 4(1), 11–27. Retrieved from https://journals.iium.edu.my/kict/index.php/jisdt/article/view/305