Web-based Cardiovascular Disease Risk Prediction using Machine Learning

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@article{1717397,
      author = {Arjun Singh, Arpita Yadav, Anjali Singh Yadav, Amit Kumar},
      title = {Web-based Cardiovascular Disease Risk Prediction using Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {137-144},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717397.pdf},
      abstract = {This paper presents a web-based cardiovascular disease (CVD) risk prediction system that combines supervised machine learning with an accessible user interface. An XGBoost classifier is trained on a cleaned Kaggle CVD dataset using eleven low-cost clinical and lifestyle features. The model achieves competitive performance (accuracy ~0.73, ROC-AUC ~ 0.80) and is deployed via a Streamlit application that provides probability-based risk categories for preliminary self-screening, illustrating an end-to- end pipeline from data preprocessing to cloud deployment.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717397}
  }