Machine learning approaches for predicting cardiovascular disease: a systematic review and meta-analysis

El-Attar, Noha E. and El-Mashad, Yehia. A. (2024) Machine learning approaches for predicting cardiovascular disease: a systematic review and meta-analysis. In: Agria Média 2023 : „A magas szintű digitális kompetencia a jövő oktatásának kulcsa”. Eszterházy Károly Katolikus Egyetem Líceum Kiadó, pp. 96-111.

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Abstract

Heart failure and heart attack are serious cardiovascular diseases that are responsible for a significant number of deaths worldwide. Early detection and accurate prediction of these diseases can be challenging, but machine learning models offer a promising approach to improve diagnosis and treatment. There has been growing interest in using machine learning models to predict heart failure and heart attack disease. These models use various types of data, such as patient demographics, medical history, vital signs, and laboratory tests, to identify patterns and predict the risk of disease in recent years. Some of the commonly used machine learning algorithms for this task includes logistic regression, decision trees, random forests, support vector machines, and neural networks. The use of machine learning models for this purpose has the potential to improve patient outcomes by enabling earlier diagnosis and targeted treatment, leading to better management of cardiovascular diseases and ultimately reducing the burden of these diseases on healthcare systems.

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