@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 {Chlioui202061,
	title = {Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12252 LNCS},
	year = {2020},
	note = {cited By 1},
	pages = {61-76},
	abstract = {Missing data imputation is an important task when dealing with crucial data that cannot be discarded such as medical data. This study evaluates and compares the impacts of two statistical and two machine learning imputation techniques when classifying breast cancer patients, using several evaluation metrics. Mean, Expectation-Maximization (EM), Support Vector Regression (SVR) and K-Nearest Neighbor (KNN) were applied to impute 18\% of missing data missed completely at random in the two Wisconsin datasets. Thereafter, we empirically evaluated these four imputation techniques when using five classifiers: decision tree (C4.5), Case Based Reasoning (CBR), Random Forest (RF), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). In total, 1380 experiments were conducted and the findings confirmed that classification using imputation based machine learning outperformed classification using statistical imputation. Moreover, our experiment showed that SVR was the best imputation method for breast cancer classification. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Breast cancer classifications, Case based reasoning, Casebased reasonings (CBR), Decision trees, Diseases, Expectation Maximization, Imputation techniques, K nearest neighbor (KNN), Learning systems, Maximum principle, Missing data imputations, Multi layer perceptron, Multilayer neural networks, Nearest neighbor search, Support vector machines, Support vector regression, Support vector regression (SVR)},
	doi = {10.1007/978-3-030-58811-3_5},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092259110\&doi=10.1007\%2f978-3-030-58811-3_5\&partnerID=40\&md5=fd5730e8014d40306df07238106d32ff},
	author = {Chlioui, I. and Abnane, I. and Idri, A.}
}
@article {Bachiri202036,
	title = {A Complete Prenatal Solution for a Reproductive Health Unit in Morocco},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1161 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {36-43},
	abstract = {A prenatal mobile Personal Health Records (mPHR), along with an Electronic Health Records (EHR) are, respectively, exploited in order to permit both the pregnant women and gynecologists or obstetricians monitor the pregnancy progress in the best conditions. For this intent, a complete solution consisting of a prenatal mPHR and an EHR were developed for the maternity {\textquotedblleft}Les Orangers{\textquotedblright} of the Avicenne University Hospital in Rabat. The complete solution provides the main functionalities of a prenatal service. Thereafter, the solution will be validated by conducting an experiment for quality and potential assessment. Hence, a recruitment process has been determined to identify the eligibility criteria to enroll participants (pregnant women and gynecologists), in addition to planning the course of the experiment. {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Complete solutions, electronic health record, Eligibility criterion, Health, Information systems, Information use, Personal health record, Pregnant woman, Recruitment process, reproductive health},
	doi = {10.1007/978-3-030-45697-9_4},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085481969\&doi=10.1007\%2f978-3-030-45697-9_4\&partnerID=40\&md5=e3d4adffd953495adbfb14b0d483c067},
	author = {Bachiri, M. and Idri, A. and Rachad, T. and Alami, H. and Redman, L.M.}
}
@article {Kadi20161,
	title = {Cardiovascular dysautonomias diagnosis using crisp and fuzzy decision tree: A comparative study},
	journal = {Studies in Health Technology and Informatics},
	volume = {223},
	year = {2016},
	note = {cited By 1},
	pages = {1-8},
	abstract = {Decision trees (DTs) are one of the most popular techniques for learning classification systems, especially when it comes to learning from discrete examples. In real world, many data occurred in a fuzzy form. Hence a DT must be able to deal with such fuzzy data. In fact, integrating fuzzy logic when dealing with imprecise and uncertain data allows reducing uncertainty and providing the ability to model fine knowledge details. In this paper, a fuzzy decision tree (FDT) algorithm was applied on a dataset extracted from the ANS (Autonomic Nervous System) unit of the Moroccan university hospital Avicenne. This unit is specialized on performing several dynamic tests to diagnose patients with autonomic disorder and suggest them the appropriate treatment. A set of fuzzy classifiers were generated using FID 3.4. The error rates of the generated FDTs were calculated to measure their performances. Moreover, a comparison between the error rates obtained using crisp and FDTs was carried out and has proved that the results of FDTs were better than those obtained using crisp DTs. {\textcopyright} 2016 The authors and IOS Press.},
	doi = {10.3233/978-1-61499-645-3-1},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971622111\&doi=10.3233\%2f978-1-61499-645-3-1\&partnerID=40\&md5=cfdd88b310ce0f50679c1d487f776be3},
	author = {Kadi, I. and Idri, A.}
}
