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September 5, 2025Indian Journal of Computer Science and Technology

Cardiovascular Disease Prediction Using Machine Learning

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Authors

MBM. Amina BegumSri Venkateswara UniversityKMKhaja Mahabubullah

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Implication

Machine learning shows improved diagnostic accuracy for cardiovascular disease in patients, suggesting better outcomes.

Key Points

  • The ML-based approach offers significant accuracy improvements over conventional diagnostic methods, enabling earlier interventions.
  • Models were validated using standard metrics like accuracy, precision, recall, F1-score, and ROC-AUC to assess performance.
  • Data preprocessing techniques like normalization, encoding, and feature selection enhance model robustness and accuracy.
  • A web-based interface developed with Streamlit allows for practical, real-time predictions in clinical settings.

Cite This Study

Begum et al. (2025) studied this question.

synapsesocial.com/papers/68bb46bd6d6d5674bccfe9c2https://doi.org/10.59256/indjcst.20250402049
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Assessment of Cardiovascular Disease Using Machine Learning2024 · 2 citations
  2. 2Cardiovascular disease prediction with machine learning techniques2024 · 9 citations
  3. 3Advancements in Cardiovascular Disease Detection: Leveraging Data Mining and Machine Learning2024 · 4 citations
  4. 4Heart Disease Prediction Using Machine Learning Algorithms: Performance Analysis2024 · 12 citations
  5. 5A Comprehensive Review of Machine Learning Algorithms in Predicting Cardiovascular Diseases2026