Utilizing The Naive Bayes Method for Heart Disorder Classification Based on Body Weight Factors

Authors

  • Mardi Turnip Faculty of Science and Technology, Universitas Prima Indonesia, Indonesia
  • Nathan Andrew Panjamotan Faculty of Science and Technology, Universitas Prima Indonesia, Indonesia
  • Agung Ramadhan Faculty of Science and Technology, Universitas Prima Indonesia, Indonesia
  • Steven Lie Faculty of Science and Technology, Universitas Prima Indonesia, Indonesia
  • Valencia Sitompul Faculty of Science and Technology, Universitas Prima Indonesia, Indonesia
  • Abdi Dharma Faculty of Science and Technology, Universitas Prima Indonesia, Indonesia
  • Arjon Turnip Department of Electrical Engineering, Universitas Padjadjaran, Indonesia

Keywords:

Naïve Bayes; Classification; Accuracy; Body Weight; Rate.

Abstract

The main cause of death which is ranked number one in Indonesia is heart disease which is usually triggered by cholesterol, diabetes and high blood pressure. Heart disorders are a significant public health concern, and understanding their correlation with body weight can aid in early diagnosis and intervention. This study presents an analysis of heart disorder classification by leveraging the Naïve Bayes method, focusing on body weight factors as key indicators. The Naïve Bayes method, a probabilistic classification technique, is applied to assess the relationship between body weight factors and the presence of heart disorders. The classification model achieved an accuracy rate of more than 95%, highlighting its potential as a valuable tool for healthcare professionals in identifying individuals at risk of heart disorders. This high level of accuracy provides a strong foundation for future research and clinical applications in the field of cardiovascular health.

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Published

2024-05-14

How to Cite

Turnip, M. ., Panjamotan, N. A. ., Ramadhan, A. ., Lie, S. ., Sitompul, V. ., Dharma, A. ., & Turnip, A. . (2024). Utilizing The Naive Bayes Method for Heart Disorder Classification Based on Body Weight Factors. Journal of Information Systems Research and Practice, 2(2), 13–21. Retrieved from https://ejournal.um.edu.my/index.php/JISRP/article/view/52392