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...

Descripción completa

Guardado en:

Detalles Bibliográficos
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
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b31d3bf5b7984135baf0aba3dd589769
record_format	dspace
spelling	oai:doaj.org-article:b31d3bf5b7984135baf0aba3dd5897692021-11-16T07:23:28ZImproving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms2626-849310.3991/ijoe.v17i11.24781https://doaj.org/article/b31d3bf5b7984135baf0aba3dd5897692021-11-01T00:00:00Zhttps://online-journals.org/index.php/i-joe/article/view/24781https://doaj.org/toc/2626-8493heart 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 predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.Halima EL HamdaouiSaid BoujrafNour El Houda ChaouiBadr AlamiMustapha MaaroufiInternational Association of Online Engineering (IAOE)articleheart disease, clinical decision systems, machine learning, random forest, adaboost algorithm, uci heart disease dataset.Computer applications to medicine. Medical informaticsR858-859.7ENInternational Journal of Online and Biomedical Engineering, Vol 17, Iss 11, Pp 60-75 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	heart disease, clinical decision systems, machine learning, random forest, adaboost algorithm, uci heart disease dataset. Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	heart disease, clinical decision systems, machine learning, random forest, adaboost algorithm, uci heart disease dataset. Computer applications to medicine. Medical informatics R858-859.7 Halima EL Hamdaoui Said Boujraf Nour El Houda Chaoui Badr Alami Mustapha Maaroufi Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms
description	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 predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.
format	article
author	Halima EL Hamdaoui Said Boujraf Nour El Houda Chaoui Badr Alami Mustapha Maaroufi
author_facet	Halima EL Hamdaoui Said Boujraf Nour El Houda Chaoui Badr Alami Mustapha Maaroufi
author_sort	Halima EL Hamdaoui
title	Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms
title_short	Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms
title_full	Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms
title_fullStr	Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms
title_full_unstemmed	Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms
title_sort	improving heart disease prediction using random forest and adaboost algorithms
publisher	International Association of Online Engineering (IAOE)
publishDate	2021
url	https://doaj.org/article/b31d3bf5b7984135baf0aba3dd589769
work_keys_str_mv	AT halimaelhamdaoui improvingheartdiseasepredictionusingrandomforestandadaboostalgorithms AT saidboujraf improvingheartdiseasepredictionusingrandomforestandadaboostalgorithms AT nourelhoudachaoui improvingheartdiseasepredictionusingrandomforestandadaboostalgorithms AT badralami improvingheartdiseasepredictionusingrandomforestandadaboostalgorithms AT mustaphamaaroufi improvingheartdiseasepredictionusingrandomforestandadaboostalgorithms
_version_	1718426625957167104

Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

Ejemplares similares