Pouriyeh, S., Vahid, S., Sannino, G., De Pietro, G., Arabnia, H. and Gutierrez, J. (2017) A Comprehensive Investigation and Comparison of Machine Learning Techniques in the Domain of Heart Disease. 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, 3-6 July 2017, 204-207.
https://doi.org/10.1109/ISCC.2017.8024530

has been cited by the following article:

• TITLE: A Comprehensive Analysis of Machine Learning Techniques for Heart Disease Prediction

  AUTHORS: Elchin Asgarov

  KEYWORDS: Machine Learning, Supervised Learning, Hybrid Model, Heart Disease Prediction, Cardiovascular Disease

  JOURNAL NAME: Open Access Library Journal, Vol.11 No.4, April 30, 2024

  ABSTRACT: Heart disease is one of the most important problems the world faces. It is an ongoing problem and it is leading to the cause of death globally. To solve this issue, predicting early heart disease is important. This research focuses on supervised machine learning techniques as a potential tool for heart disease prediction. This study has done a comprehensive review of 30 articles published between 1997 to 2023 about machine learning techniques to predict heart disease. The common problem is that authors use different data sets, and different numbers of parameters to train and test these models. These two factors could affect the model’s accuracy. To compare different models, I only used articles that analyze more than one method using the same data to prevent bias. Some traditional machine learning methods such as Artificial Neural Network, and K-Nearest Neighbor demonstrated significant variation in accuracy, occasionally reaching as high as 100% but sometimes falling below 60% in specific situations which is inconsistent. Compared to these models, Hybrid Models show consistent accuracy, with a minimum accuracy rate of 88%, suggesting that they could be a better approach to predicting heart disease.