Abstract & Overview
Heart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms, which leads to severe concerns such as heart failure and stroke and kidney failure. In this regard, the authors highlight an amount of literature considered the most practical in utilizing machine learning techniques in predicting heart disease. Twenty articles were chosen out of fifty articles gathered and summarised in a table form. The main goal is to make this article a reference that can be utilized in the future to assist healthcare workers in studying these techniques with ease and saving time and effort on them. This article has concluded that machine learning techniques have a significant and influential role in analyzing disease data, predicting heart disease, and assisting decision-making. In addition, these techniques can analyze data that reaches millions of cohorts.