Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis

This systematic review relied on the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement and 37 relevant studies. The literature search used search engines including PubMed, Hindawi, SCOPUS, IEEE Xplore, Web of Science, Google Scholar, Wiley Online, Jstor, Taylor and Francis, Ebscohost, and ScienceDirect. This study focused on four aspects: Machine Learning Algorithms, datasets, best-performing algorithms, and software used in coronary heart disease (CHD) predictions. The empirical articles never mentioned 'Reinforcement Learning,' a promising aspect of Machine Learning. Ensemble algorithms showed reasonable accuracy rates but were not common, whereas deep neural networks were poorly represented. Only a few papers applied primary datasets (4 of 37). Logistic Regression (LR), Deep Neural Network (DNN), K-Means, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and boosting algorithms were the best performing algorithms. This systematic review will be valuable for researchers predicting coronary heart disease using machine learning techniques.

@article{Kutiame2022,
  title     = {Application of Machine Learning Algorithms in Coronary Heart Disease: A Systematic Literature Review and Meta-Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {6},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Solomon Kutiame and Richard Millham and Adebayor Felix Adekoya and Mark Tettey and Benjamin Asubam Weyori and Peter Appiahene},
  doi       = {10.14569/IJACSA.2022.0130620},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130620}
}