CLINICAL IMPLICATIONS OF BIG DATA IN PREDICTING CARDIOVASCULAR DISEASE USING SMOTE FOR HANDLING IMBALANCED DATA

Authors

  • Koteswararao Dondapati Author

DOI:

https://doi.org/10.48047/

Keywords:

Cardiovascular Diseases (CVD), Synthetic Minority Oversampling Technique (SMOTE), Imbalanced Data, Big Data Analytics, Prediction Models

Abstract

Cardiovascular diseases (CVDs) are the main cause of death worldwide, requiring early identification to improve patient outcomes. Imbalanced datasets make it difficult to anticipate accurately since they frequently underestimate disease cases

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Published

2020-09-24

Issue

Vol. 11 No. 9 (2020): volume 11 issue 9 (2020)

Section

Articles