Clinical Support System for Cardiovascular Disease Forecasting Using ECG


  • Mohammed Altaf Ahmed Prince Sattam Bin Abdulaziz University image/svg+xml
  • Q S Tasmeem Naz Shadan College of Engineering
  • Raghav Agarwal Vellore Institute of Technology University image/svg+xml
  • Mannava Yesubabu Vardhaman College of Engineering image/svg+xml
  • Rajesh Tulasi Koneru Lakshmaiah Education Foundation image/svg+xml



Machine Learning, heart failure, decision support system, ensemble classifiers, cross-validation


INTRODUCTION: Heart failure is a chronic condition that affects many people worldwide. Regrettably, it is now the biggest cause of mortality globally, and it is becoming more common. Before a cardiac event, early diagnosis of heart disease is challenging. Although healthcare institutions like hospitals and clinics have access to a wealth of heart disease data, it is rarely used to uncover underlying trends.

OBJECTIVES: Algorithms for machine learning (ML) can turn this medical data into insightful information. These methods are used to create decision support systems (DSS) that can gain knowledge from the past and advance. It is essential to use an effective ML-based technique to identify early heart failure and take preventive action to address this worldwide issue. Accurately identifying heart illness is our main goal in this study.

METHODS: For this work, we benchmark different datasets on heart illness, and we use feature engineering approaches to pick the most pertinent qualities for improved performance. Additionally, we assess nine ML methods using critical parameters including precision, f-measure, sensitivity, specificity, and accuracy.

RESULTS: Iterative tests are carried out to evaluate the efficacy of different algorithms. With a flawless cross-validation accuracy score of 99.51% and 100% in all other metrics, our suggested Decision Tree approach performs better than other ML models and cutting-edge studies.

CONCLUSION: Each methodology used in our study is validated using cross-validation techniques. The medical community benefits greatly from this research study.


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How to Cite

Ahmed MA, Naz QST, Agarwal R, Yesubabu M, Tulasi R. Clinical Support System for Cardiovascular Disease Forecasting Using ECG. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 18 [cited 2024 Apr. 25];10. Available from:

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