Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Adedayo Ogunpola (Corresponding / Lead Author), Faisal Saeed (Corresponding / Lead Author), Shadi Basurra, Abdullah Albarrak, Sultan Qasem

    Research output: Contribution to journalArticlepeer-review

    6 Citations (SciVal)


    Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease.
    Original languageEnglish
    Article number144
    Pages (from-to)1-19
    Number of pages19
    Issue number2
    Publication statusPublished (VoR) - 8 Apr 2024


    The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Grant Number IMSIU-RG23077.

    FundersFunder number
    Deanship of Scientific Research, Imam Mohammed Ibn Saud Islamic UniversityIMSIU-RG23077


      • cardiovascular diseases
      • deep learning
      • disease detection
      • ensemble learning
      • heart diseases
      • machine learning
      • XGBoost


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