@article {Benhar2022791,
	title = {Univariate and Multivariate Filter Feature Selection for Heart Disease Classification},
	journal = {Journal of Information Science and Engineering},
	volume = {38},
	number = {4},
	year = {2022},
	note = {cited By 0},
	pages = {791-803},
	abstract = {Feature selection (FS) is a data preprocessing task that can be applied before the classification phase, and aims at improving the performance and interpretability of classifiers by finding only a few highly informative features. The present study aims at evaluating and comparing the performances of six univariate and two multivariate filter FS techniques for heart disease classification. The FS techniques were evaluated with two white-box and two black-box classification techniques using five heart disease datasets. Furthermore, this study deals with the setting of the hyperparameters{\textquoteright} values of the four classifiers. This study evaluates 600 variants of classifiers. Results show that white-box classification techniques such as K-Nearest Neighbors and Decision Trees can be very competitive with black-box ones when hyperparameters{\textquoteright} optimization and feature selection were applied. {\textcopyright} 2022 Institute of Information Science. All rights reserved.},
	keywords = {Black boxes, Cardiology, Classification (of information), Data preprocessing, Decision trees, disease classification, Diseases, feature selection, Features selection, Filter, Heart, heart disease, Nearest neighbor search, Performance, Selection techniques, Univariate, White box},
	doi = {10.6688/JISE.202207_38(4).0006},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137170374\&doi=10.6688\%2fJISE.202207_38\%284\%29.0006\&partnerID=40\&md5=f75bca8ff78e6782d4c11e88d338784f},
	author = {Benhar, H. and Hosni, M. and Idri, A.}
}
@article {Benhar2020,
	title = {Data preprocessing for heart disease classification: A systematic literature review.},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {195},
	year = {2020},
	note = {cited By 25},
	abstract = {Context: Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual{\textquoteright}s life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems. Objective: The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate. Method: A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria. Results: The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Cardiology, Classification (of information), Classification technique, classifier, clinical practice, clinical research, Computer aided diagnosis, data classification, Data mining, Data preprocessing, data processing, Decision support systems, Deep learning, Diagnosis decision, diagnostic accuracy, disease classification, Diseases, empiricism, evidence based practice, feature selection, Heart, heart disease, Heart Diseases, High dimensionality, human, Humans, intermethod comparison, Machine learning, Performance of classifier, prediction, Prediction systems, Preprocessing techniques, publication, Review, Support vector machines, Systematic literature review, Systematic Review, task performance},
	doi = {10.1016/j.cmpb.2020.105635},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087500300\&doi=10.1016\%2fj.cmpb.2020.105635\&partnerID=40\&md5=cae53ce36903d5d8b817ec96deb39b45},
	author = {Benhar, H. and Idri, A. and L Fern{\'a}ndez-Alem{\'a}n, J.}
}
@conference {Benhar2020391,
	title = {Impact of threshold values for filter-based univariate feature selection in heart disease classification},
	booktitle = {HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020},
	year = {2020},
	note = {cited By 2},
	pages = {391-398},
	abstract = {In the last decade, feature selection (FS), was one of the most investigated preprocessing tasks for heart disease prediction. Determining the optimal features which contribute more towards the diagnosis of heart disease can reduce the number of clinical tests needed to be taken by a patient, decrease the model cost, reduce the storage requirements and improve the comprehensibility of the induced model. In this study a comparison of three filter feature ranking methods was carried out. Feature ranking methods need to set a threshold (i.e. the percentage of the number of relevant features to be selected) in order to select the final subset of features. Thus, the aim of this study is to investigate if there is a threshold value which is an optimal choice for three different feature ranking methods and four classifiers used for heart disease classification in four heart disease datasets. The used feature ranking methods and selection thresholds resulted in optimal classification performance for one or more classifiers over small and large heart disease datasets. The size of the dataset takes an important role in the choice of the selection threshold. {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Biomedical engineering, Cardiology, Classification (of information), Clinical tests, Diagnosis, Diseases, Feature extraction, Feature ranking, Heart, heart disease, Large dataset, Medical informatics, Optimal choice, Optimal classification, Relevant features, Storage requirements, Threshold-value},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083712586\&partnerID=40\&md5=4656d8b952f7c60387d4495c737c5a6d},
	author = {Benhar, H. and Idri, A. and Hosni, M.}
}
@article {Benhar2019,
	title = {A Systematic Mapping Study of Data Preparation in Heart Disease Knowledge Discovery},
	journal = {Journal of Medical Systems},
	volume = {43},
	number = {1},
	year = {2019},
	note = {cited By 0},
	doi = {10.1007/s10916-018-1134-z},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058606352\&doi=10.1007\%2fs10916-018-1134-z\&partnerID=40\&md5=f92d6f6b431ad7798a78fb05c7a5624d},
	author = {Benhar, H. and Idri, A. and Fernandez-Aleman, J.L.}
}
@article {Benhar20181208,
	title = {Data preprocessing for decision making in medical informatics: Potential and analysis},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {746},
	year = {2018},
	pages = {1208-1218},
	doi = {10.1007/978-3-319-77712-2_116},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045323949\&doi=10.1007\%2f978-3-319-77712-2_116\&partnerID=40\&md5=86d2827399aedd6683b71d8893a2d49c},
	author = {Benhar, H. and Idri, A. and Fernandez-Aleman, J.L.}
}
@article {Idri201869,
	title = {A systematic map of medical data preprocessing in knowledge discovery},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {162},
	year = {2018},
	pages = {69-85},
	doi = {10.1016/j.cmpb.2018.05.007},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046706780\&doi=10.1016\%2fj.cmpb.2018.05.007\&partnerID=40\&md5=30324a8f7b2702347d51c7c56b55b294},
	author = {Idri, A. and Benhar, H. and Fernandez-Aleman, J.L. and Kadi, I.}
}
