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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 BegumKMKhaja Mahabubullah

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Overview

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/68c23966b210217d6477ba1dhttps://doi.org/10.59256/indjcst.20250402049
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Also Consider

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  1. 1Heart disease risk prediction using machine learning model2025
  2. 2Performance Evaluation of Machine Learning Models for Cardiovascular Disease Prediction2025 · 2 citations
  3. 3DATA-DRIVEN PREDICTIVE MODELING FOR EARLY CARDIOVASCULAR DISEASE IDENTIFICATION2026
  4. 4DATA-DRIVEN PREDICTIVE MODELING FOR EARLY CARDIOVASCULAR DISEASE IDENTIFICATION2026
  5. 5Comparative of machine learning methods for detecting cardiovascular disease2026