Prediction of Heart Failures using Machine Learning: A Systematic Literature Review
DOI:
https://doi.org/10.15381/rpcs.v6i2.29141Keywords:
Artificial Intelligence, Machine Learning, heart failuresAbstract
The development of Artificial Intelligence (AI) is currently at its peak, sparking the interest of the scientific community to conduct studies using Machine Learning (ML), a branch of AI. Through trained algorithms or models, ML can predict heart failures. According to the literature review conducted, it was found that the most commonly used variables in studies to predict heart failures are age, sex, fasting glucose, systolic blood pressure, and LDL cholesterol. Additionally, data preprocessing is carried out, which involves various phases, with the most commonly used being Rescaling, Data Cleaning, Clustering, Data Encoding, and Outlier Detection. It was also observed that most studies propose their own methodologies, with the most frequently employed phases being Preprocessing, Model Application, Results Analysis, Train-test, Data Classification, and Model or Algorithm Selection.
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Copyright (c) 2024 Jesús Sebastian Senmache Ly, Yamil Alexander Quiñones Nieto
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