| Abstrak/Abstract |
Heart disease, categorized as a cardiovascular
condition, stands as a prominent factor in worldwide mortality,
accounting for approximately 32% of all deaths globally. It
manifests when the buildup of arterial plaque obstructs the
circulation of blood to the heart or brain, potentially resulting
in a stroke or heart attack. Early identification of heart disease
is needed to reduce mortality rates and improve decision-
making in the prevention and treatment of high-risk individuals.
This can be done by using a prediction model. To offer a
comprehensive examination of machine learning research about
the prediction of cardiac disease, a systematic literature review
(SLR) was undertaken. Based on these papers, researchers can
focus on four main research questions. The UCI dataset was
commonly used in 25 out of 32 papers. Random forest was the
most popular machine learning algorithm, used in six papers.
The limitations identified by the authors mainly revolve around
the dataset, including the need for a larger one. Other
limitations involve the inability to detect subtle changes and the
use of oversimplified algorithms for prediction. To address these
limitations, future research can explore new and larger datasets,
experiment with different algorithms, and consider
advancements in software and hardware. |
