ARTIFICIAL LEARNING BASED ON KERNEL SVM FOR THE PREDICTION OF CARDIOVASCULAR DISEASE HYPERTENSION

Authors

  • Patient MUSUBAO SWAMBI Department of Computer Science, Université Notre-Dame du Kasayi, Kananga, R.D. Congo
  • Albert Ntumba Nkongolo Department of Mathematics, Statistics and Computer Science, University of Kinshasa, Kinshasa, Democratic Republic of Congo
  • Pierre Kafunda Katalay Department of Mathematics, Statistics and Computer Science, University of Kinshasa, Kinshasa, Democratic Republic of Congo
  • Rostin Mabela Matendo Makengo Department of Mathematics, Statistics and Computer Science, University of Kinshasa, Kinshasa, Democratic Republic of Congo
  • Eugène Mbuyi Mukendi Department of Mathematics, Statistics and Computer Science, University of Kinshasa, Kinshasa, Democratic Republic of Congo

DOI:

https://doi.org/10.33480/techno.v22i1.6011

Keywords:

Cardiovascular Disease Prediction, Hypertension Prediction, Kernel SVM, Machine Learning, Radial Basis Function, Support Vector Machine

Abstract

Hypertension, a critical risk factor for cardiovascular diseases, requires accurate early detection for effective management. This study examines the application of kernel-based Support Vector Machines (SVM) for predicting hypertension, utilizing advanced machine learning techniques to address the complex, non-linear relationships inherent in healthcare data. By employing various kernel functions, such as the radial basis function (RBF) and polynomial kernels, the study aims to enhance the model's ability to capture and interpret the nuanced patterns associated with hypertension risk. The research utilizes a diverse dataset that includes demographic, physiological, and lifestyle variables, applying kernel SVM to predict hypertension outcomes. Performance is evaluated through rigorous cross-validation, with metrics including accuracy, precision, recall, and F1-score. The findings indicate that kernel SVMs significantly outperform traditional linear models, offering superior prediction accuracy and robustness. This study highlights the potential of advanced machine learning methods in improving early detection and personalized risk assessment for hypertension, ultimately supporting more effective management strategies and better cardiovascular health outcomes.

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Published

2025-03-14

How to Cite

MUSUBAO SWAMBI, P., Ntumba Nkongolo, A., Kafunda Katalay, P., Mabela Matendo Makengo, R., & Mbuyi Mukendi, E. (2025). ARTIFICIAL LEARNING BASED ON KERNEL SVM FOR THE PREDICTION OF CARDIOVASCULAR DISEASE HYPERTENSION. Jurnal Techno Nusa Mandiri, 22(1), 28–34. https://doi.org/10.33480/techno.v22i1.6011

Issue

Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of March 2025

Section

Articles

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Copyright (c) 2025 Patient MUSUBAO SWAMBI, Albert Ntumba Nkongolo, Pierre Kafunda Katalay, Rostin Mabela Matendo Makengo, Eugène Mbuyi Mukendi

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