Algorithm for the prediction of cardiovascular disease (CVD)

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 15 | Issue : 02 | Page :
    By

    Gaurav Gopal Gupta,

  1. Research Scholar, Department of Computer Application, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India

Abstract

cardiovascular diseases (CVD) still claim a significant number of deaths globally and remain the number one killer with an annual death toll of nearly 17.9 million. While several medical advancements have been made, an early diagnosis is still hard to obtain, which often leads to worsening conditions and intricate treatment options. With the advancement of modern technology, Machine learning has demonstrated to be a miraculous tool which can greatly impact early detection and diagnosis with its efficient predictions. This research uses a comparison of machine learning algorithms to analyze and predict CVD. Taking advantage of the Kaggle Heart Disease Dataset, which is obtained from several UCI repositories, we assess the performance of various classification methods including Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and Naive Bayes. The dataset needs preprocessing to manage missing values, normalize the features, and pick specific pertinent variables to enhance the model’s accuracy. In order to conduct an objective assessment, we also consider accuracy, precision, recall, F1-score, and AUC – ROC as metrics for performance evaluation. It was revealed that more sophisticated techniques, including ensemble models such as Random Forest and Gradient Boosting, have greater prediction accuracy than standard models, including Logistic Regression, which is, however, more interpretable and clinically relevant. This study pays an important attention to the fact that the most sophisticated algorithm is not necessarily the most useful due to the limited constraints in most healthcare settings. Because of machine learning, this research could also advance automated risk assessment tools that would support healthcare providers in balancing risks and benefits. Subsequent work will concentrate on broader datasets, understanding deep learning, the optimization of Artifical intelligence model and emphasizing practical usability with the aim to improve clinical practice predictive accuracy.

Keywords: CVD, heart disease, UCI, Machine Learning Algorithms, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM)

[This article belongs to Research and Reviews : A Journal of Immunology ]

How to cite this article:
Gaurav Gopal Gupta. Algorithm for the prediction of cardiovascular disease (CVD). Research and Reviews : A Journal of Immunology. 2025; 15(02):-.
How to cite this URL:
Gaurav Gopal Gupta. Algorithm for the prediction of cardiovascular disease (CVD). Research and Reviews : A Journal of Immunology. 2025; 15(02):-. Available from: https://journals.stmjournals.com/rrjoi/article=2025/view=211467


References

  1. Singh P, Gupta N. Logistic regression analysis of heart disease data. Int J Med Inform. 2014;64(2):29–34.
  2. Rawat S. Heart disease prediction using machine learning techniques: A comparative study. Procedia Comput Sci. 2016;104(3):314–21.
  3. Soni SK, Prasad R. Predicting cardiovascular disease using ensemble learning. Procedia Comput Sci. 2016; 58:234–40.
  4. Lee S, Chu Y, Ryu J, Park YJ, Yang S, Koh SB. Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: a systematic review and meta-analysis. Yonsei medical journal. 2022 Jan 6;63(Suppl): S93.
  5. Shishehbori F, Awan Z. Enhancing cardiovascular disease risk prediction with machine learning models. Comput Math Methods Med. 2019;78(6):99–108.
  6. Sharma D, et al. heart disease prediction using machine learning: A case study. Healthc Technol Lett. 2019;10(6):215–21.
  7. Aziz MJA, et al. Predicting heart disease using machine learning algorithms: A comprehensive review. J Med Syst. 2019;43(8):199–212.
  8. Shishehbori and Awan. Machine learning-based identification of patients with a cardiovascular defect. J Big Data. 2018;5(6):11–29.
  9. Chaurasia PH, Pal SM. Heart disease prediction using hybrid models based on genetic algorithms and SVM. J Appl Comput Res. 2020;45(4):321–35.
  10. Gupta NS. Analysis of cardiovascular disease prediction using machine learning algorithms. IEEE Access. 2020;8:123456–67.
  11. Sharma M, Pandya JD, Thakkar R, Sharma RK, Chopra A, Tyagi RS. Comparative Analysis of Machine Learning Algorithms For Heart Disease Prediction: A Focus On Feature Importance and Model Performance. In2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N) 2024 Dec 16 (pp. 1433-1437). IEEE.
  12. Gupta M, Kumar A. Comparison of neural networks and random forests for heart disease prediction. Int J Med Inform. 2021; 145:104313.
  13. Barrozo LS, et al. Exploring machine learning models to predict heart disease risk. J Health Inform. 2020;7(3):215–20.
  14. Ghosh S, et al. A comprehensive study of machine learning algorithms for heart disease prediction. Healthc Anal. 2021; 14:99–112.
  15. Chen C, et al. Comparison of machine learning algorithms in the prediction of cardiovascular diseases: A case study using UCI heart disease data. Comput Biol Med. 2021;99(1):55–63.
  16. Gupta A, Kumar S. Cardiovascular disease prediction using neural networks and random forest: A comparative study. Int J Comput Biol. 2021;6(4):143–55.
  17. World Health Organization. Cardiovascular diseases [Internet]. Available from: https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1. [Accessed 2025 Feb 1].
  18. World Heart Federation. Deaths from cardiovascular disease surged 60% globally [Internet]. Available from: https://world-heart-federation.org/news/deaths-from-cardiovascular-disease-surged-60-globally-over-the-last-30-years-report/. [Accessed 2025 Feb 1].
  19. World Heart Federation. What is CVD? [Internet]. Available from: https://world-heart-federation.org/what-is-cvd/. [Accessed 2025 Feb 1].
  20. Heart disease data set [Internet]. Available from: https://www.kaggle.com/datasets/redwankarimsony/heart-disease-data. [Accessed 2025 Feb 1].
Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 10/03/2025
Accepted 03/04/2025
Published 27/05/2025
Publication Time 78 Days
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