@article {ElOuassif202150,
	title = {Classification techniques in breast cancer diagnosis: A systematic literature review},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	volume = {9},
	number = {1},
	year = {2021},
	note = {cited By 12},
	pages = {50-77},
	abstract = {Data mining (DM) consists in analysing a~set of observations to find unsuspected relationships and then summarising the data in new ways that are both understandable and useful. It has become widely used in various medical fields including breast cancer (BC), which is the most common cancer and the leading cause of death among women~worldwide.~BC diagnosis is a~challenging medical task and many studies have attempted to apply classification techniques to it. The objective of the present study is to identify studies on classification techniques~in~BC diagnosis and to analyse them from~three perspectives: classification techniques used, accuracy of the classifiers, and comparison of performance. We performed a~systematic literature review (SLR) of 176 selected studies published between January~2000 and November~2018. The results show that, of the nine classification techniques investigated, artificial neural networks, support vector machines and decision trees were the most frequently used. Moreover, artificial neural networks, support vector machines and ensemble classifiers performed better than the other techniques, with median accuracy values of 95\%, 95\% and 96\% respectively. Most of the selected studies (57.4\%) used datasets containing different types of images such as mammographic, ultrasound, and microarray images. {\textcopyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Article, Artificial intelligence, artificial neural network, Breast Cancer, Breast cancer diagnosis, cancer diagnosis, cause of death, Causes of death, Classification (of information), Classification technique, Comparison of performance, Computer aided diagnosis, data extraction, Data mining, data synthesis, decision tree, Decision trees, Diseases, human, k nearest neighbor, Machine learning, Medical fields, Medical informatics, Network support, Neural networks, publication, qualitative research, Quality control, support vector machine, Support vector machine classifiers, Support vector machines, Support vectors machine, Systematic literature review, Systematic Review, validity},
	doi = {10.1080/21681163.2020.1811159},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098842973\&doi=10.1080\%2f21681163.2020.1811159\&partnerID=40\&md5=9a48998b1c44d263863efcfb25f9920f},
	author = {ElOuassif, B. and Idri, A. and Hosni, M. and Abran, 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.}
}
