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An ensemble model for Heart disease data sets: a generalized model

Published: 09 May 2016 Publication History

Abstract

Diagnosing Heart diseases is one of the problems that require high level of accurate analysis and prediction. Using ensemble methods in decision support systems provide an important help in analyzing this type of diseases. Different data is extracted from different research laboratories referring to the same disease. This requires further analysis and rework in detecting the best ensemble. Trying different combinations of classification techniques for every data set is not the best solution and consumes a lot of time and effort. The proposed framework seeks the best ensemble combination method suitable for diagnosing heart diseases. This ensemble is a majority vote based method and is designed for every data set belongs to the domain of heart disease. The experimental analysis is applied on two benchmark data sets extracted from two different resources. The classification accuracy results reached percentages higher than 90% accuracy. Observations reveal that the best combination for both datasets is mostly the combinations which the Naive Bayes as one of its classifiers with accuracy of 92%.

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cover image ACM Other conferences
INFOS '16: Proceedings of the 10th International Conference on Informatics and Systems
May 2016
347 pages
ISBN:9781450340625
DOI:10.1145/2908446
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 May 2016

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Author Tags

  1. Classification
  2. Ensemble technique
  3. Heart Sound

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  • (2023)An Ensemble Approach for Prediction of Cardiovascular Disease Using Meta Classifier Boosting AlgorithmsInternational Journal of Data Warehousing and Mining10.4018/IJDWM.31614518:1(1-29)Online publication date: 13-Jan-2023
  • (2023)Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical MethodsBiology10.3390/biology1201011712:1(117)Online publication date: 11-Jan-2023
  • (2023)DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series FeaturesIEEE Access10.1109/ACCESS.2023.332570511(117643-117655)Online publication date: 2023
  • (2022)Ensemble framework for cardiovascular disease predictionComputers in Biology and Medicine10.1016/j.compbiomed.2022.105624146:COnline publication date: 1-Jul-2022
  • (2022)Heart Disease Classification Using Regional Wall Thickness by Ensemble ClassifierBioinformatics and Medical Applications10.1002/9781119792673.ch6(99-116)Online publication date: 23-Mar-2022
  • (2021)Heart Disease Classification using Novel Heterogeneous Ensemble2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)10.1109/BHI50953.2021.9508516(1-4)Online publication date: 27-Jul-2021
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  • (2020)A systematic mapping study for ensemble classification methods in cardiovascular diseaseArtificial Intelligence Review10.1007/s10462-020-09914-6Online publication date: 1-Oct-2020
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