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Cardiovascular Disease Analysis and Prediction using Machine Learning

  • Unique Paper ID: 180183
  • Volume: 12
  • Issue: 1
  • PageNo: 262-269
  • Abstract:
  • Cardiovascular disease (CVD) prediction using machine learning has gained momentum due to the availability of large clinical datasets. While existing literature extensively explores conventional classifiers such as Logistic Regression, Decision Tree, and Random Forest, this study investigates the impact of incorporating more advanced ensemble techniques, including Gradient Boosting Machine (GBM) and XGBoost, alongside detailed visual data diagnostics. The comparative performance evaluation highlights the benefits of integrating multiple classifiers and emphasizes the importance of data-driven insights in feature distribution and correlation. The study underscores the superiority of Logistic Regression for this dataset but also explores potential improvements through ensemble learning
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  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 262-269

Cardiovascular Disease Analysis and Prediction using Machine Learning

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