A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques

Heart disease is considered as one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. Currently, the recent development in medical supportive technologies...

Descripción completa

Detalles Bibliográficos
Autores principales: Abdeldjouad, Fatma Zahra, Brahami, Menaouer, Matta, Nada
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313286/
http://dx.doi.org/10.1007/978-3-030-51517-1_26
_version_ 1783549917114925056
author Abdeldjouad, Fatma Zahra
Brahami, Menaouer
Matta, Nada
author_facet Abdeldjouad, Fatma Zahra
Brahami, Menaouer
Matta, Nada
author_sort Abdeldjouad, Fatma Zahra
collection PubMed
description Heart disease is considered as one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. Currently, the recent development in medical supportive technologies based on data mining, machine learning plays an important role in predicting cardiovascular diseases. In this paper, we propose a new hybrid approach to predict cardiovascular disease using different machine learning techniques such as Logistic Regression (LR), Adaptive Boosting (AdaBoostM1), Multi-Objective Evolutionary Fuzzy Classifier (MOEFC), Fuzzy Unordered Rule Induction (FURIA), Genetic Fuzzy System-LogitBoost (GFS-LB) and Fuzzy Hybrid Genetic Based Machine Learning (FH-GBML). For this purpose, the accuracy and results of each classifier have been compared, with the best classifier chosen for a more accurate cardiovascular prediction. With this objective, we use two free software (Weka and Keel).
format Online
Article
Text
id pubmed-7313286
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-73132862020-06-24 A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques Abdeldjouad, Fatma Zahra Brahami, Menaouer Matta, Nada The Impact of Digital Technologies on Public Health in Developed and Developing Countries Article Heart disease is considered as one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. Currently, the recent development in medical supportive technologies based on data mining, machine learning plays an important role in predicting cardiovascular diseases. In this paper, we propose a new hybrid approach to predict cardiovascular disease using different machine learning techniques such as Logistic Regression (LR), Adaptive Boosting (AdaBoostM1), Multi-Objective Evolutionary Fuzzy Classifier (MOEFC), Fuzzy Unordered Rule Induction (FURIA), Genetic Fuzzy System-LogitBoost (GFS-LB) and Fuzzy Hybrid Genetic Based Machine Learning (FH-GBML). For this purpose, the accuracy and results of each classifier have been compared, with the best classifier chosen for a more accurate cardiovascular prediction. With this objective, we use two free software (Weka and Keel). 2020-05-31 /pmc/articles/PMC7313286/ http://dx.doi.org/10.1007/978-3-030-51517-1_26 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
spellingShingle Article
Abdeldjouad, Fatma Zahra
Brahami, Menaouer
Matta, Nada
A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques
title A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques
title_full A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques
title_fullStr A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques
title_full_unstemmed A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques
title_short A Hybrid Approach for Heart Disease Diagnosis and Prediction Using Machine Learning Techniques
title_sort hybrid approach for heart disease diagnosis and prediction using machine learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313286/
http://dx.doi.org/10.1007/978-3-030-51517-1_26
work_keys_str_mv AT abdeldjouadfatmazahra ahybridapproachforheartdiseasediagnosisandpredictionusingmachinelearningtechniques
AT brahamimenaouer ahybridapproachforheartdiseasediagnosisandpredictionusingmachinelearningtechniques
AT mattanada ahybridapproachforheartdiseasediagnosisandpredictionusingmachinelearningtechniques
AT abdeldjouadfatmazahra hybridapproachforheartdiseasediagnosisandpredictionusingmachinelearningtechniques
AT brahamimenaouer hybridapproachforheartdiseasediagnosisandpredictionusingmachinelearningtechniques
AT mattanada hybridapproachforheartdiseasediagnosisandpredictionusingmachinelearningtechniques

Ejemplares similares