Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to pred...

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Autores principales: Halima EL Hamdaoui, Said Boujraf, Nour El Houda Chaoui, Badr Alami, Mustapha Maaroufi
Formato: article
Lenguaje:EN
Publicado: International Association of Online Engineering (IAOE) 2021
Materias:
heart disease, clinical decision systems, machine learning, random forest, adaboost algorithm, uci heart disease dataset.
Computer applications to medicine. Medical informatics
R858-859.7
Acceso en línea:https://doaj.org/article/b31d3bf5b7984135baf0aba3dd589769
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