@article {Nakach2022446,
	title = {Deep Hybrid AdaBoost Ensembles for Histopathological Breast Cancer Classification},
	journal = {Lecture Notes in Networks and Systems},
	volume = {468 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {446-455},
	abstract = {Breast cancer (BC) is the most common diagnosed cancer type and one of the top leading causes of death in women worldwide. The early diagnosis of this type of cancer is the main driver of high survival rate. This paper aims to use homogenous ensemble learning and transfer learning for binary classification of BC histological images over the four-magnification factor (MF) values of the BreakHis dataset: 40X, 100X, 200X, and 400X. The proposed ensembles are implemented using a hybrid architecture (HA) that combines: (1) three of the most recent deep learning (DL) techniques as feature extractors (FE): DenseNet_201, Inception_V3, and MobileNet_V2, and (2) the boosting method AdaBoost with Decision Tree (DT) as a base learner. The study evaluated and compared: the ensembles designed with the same HA but with different number of trees (50, 100, 150 and 200), the single DT classifiers with the best AdaBoost ensembles and the best AdaBoost ensembles of each FE over each MF. The empirical evaluations used: four classification performance criteria (accuracy, recall, precision and F1-score), 5-fold cross-validation, Scott Knott (SK) statistical test to select the best cluster of the outperforming models, and Borda Count voting system to rank the best performing ones. Results showed the potential of combining DL techniques for FE and AdaBoost boosting method to classify BC in malignant and benign tumors, furthermore the AdaBoost ensemble constructed using 200 trees, DenseNet_201 as FE and MF 200X achieved the best mean accuracy value with 90.36\%. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04826-5_45},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130279063\&doi=10.1007\%2f978-3-031-04826-5_45\&partnerID=40\&md5=59d8d87203f77eed9f4da56cae519226},
	author = {Nakach, F.-Z. and Zerouaoui, H. and Idri, A.}
}
@article {Zerouaoui2022,
	title = {Deep hybrid architectures for binary classification of medical breast cancer images},
	journal = {Biomedical Signal Processing and Control},
	volume = {71},
	year = {2022},
	note = {cited By 16},
	abstract = {The diagnosis of breast cancer in the early stages significantly decreases the mortality rate by allowing the choice of adequate treatment. This study developed and evaluated twenty-eight hybrid architectures combining seven recent deep learning techniques for feature extraction (DenseNet 201, Inception V3, Inception ReseNet V2, MobileNet V2, ResNet 50, VGG16, and VGG19), and four classifiers (MLP, SVM, DT, and KNN) for a binary classification of breast pathological images over the BreakHis and FNAC datasets. The designed architectures were evaluated using: (1) four classification performance criteria (accuracy, precision, recall, and F1-score), (2) Scott Knott (SK) statistical test to cluster the proposed architectures and identify the best cluster of the outperforming architectures, and (3) the Borda Count voting method to rank the best performing architectures. The results showed the potential of combining deep learning techniques for feature extraction and classical classifiers to classify breast cancer in malignant and benign tumors. The hybrid architecture using the MLP classifier and DenseNet 201 for feature extraction (MDEN) was the top performing architecture with higher accuracy values reaching 99\% over the FNAC dataset, 92.61\%, 92\%, 93.93\%, and 91.73\% over the four magnification factor values of the BreakHis dataset: 40X, 100X, 200X, and 400X, respectively. The results of this study recommend the use of hybrid architectures using DenseNet 201 for the feature extraction of the breast cancer histological images because it gave the best results for both datasets BreakHis and FNAC, especially when combined with the MLP classifier. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {accuracy, algorithm, Article, augmentation index, Binary classification, biopsy technique, Breast Cancer, Breast Cancer Histopathological Image Classification, Classification (of information), Classification algorithm, classifier, Cluster computing, clustering algorithm, Clustering algorithms, colloid carcinoma, Computer aided diagnosis, Computer architecture, construct validity, contrast limited adaptive histogram equalization, Convolutional neural network, Convolutional neural networks, deep hybrid architecture, Deep learning, Deep neural networks, Diseases, ductal carcinoma, external validity, Extraction, F1 score, Feature extraction, Features extraction, feed forward neural network, fibroadenoma, fine needle aspiration biopsy, histogram, Histological images, histology, Hybrid architectures, Image classification, image processing, Images processing, internal validity, learning algorithm, Learning algorithms, Learning techniques, lobular carcinoma, Machine learning, measurement precision, Medical imaging, MLP classifiers, Mortality rate, Network architecture, papillary carcinoma, Pathological images, phyllodes tumor, recall, residual neural network, scoring system, Scott Knott, Support vector machines},
	doi = {10.1016/j.bspc.2021.103226},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125746862\&doi=10.1016\%2fj.bspc.2021.103226\&partnerID=40\&md5=b9b74d0dcb135861bc2e3d820f836efa},
	author = {Zerouaoui, H. and Idri, A.}
}
@article {Lahmar2022,
	title = {Deep hybrid architectures for diabetic retinopathy classification},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	year = {2022},
	note = {cited By 0},
	abstract = {Diabetic retinopathy (DR) is the most severe ocular complication of diabetes. It leads to serious eye complications such as vision impairment and blindness. A computer-aided diagnosis may help in the early detection of this disease, which increases the chances of treating it efficiently. This paper carried out an empirical evaluation of the performances of 28 deep hybrid architectures for an automatic binary classification of the referable diabetic retinopathy, and compared them to seven end-to-end deep learning (DL) architectures. For the hybrid architectures, we combined seven DL techniques for feature extraction (DenseNet201, VGG16, VGG19, MobileNet_V2, Inception_V3, Inception_ResNet_V2 and ResNet50) and four classifiers (SVM, MLP, DT and KNN). For the end-to-end DL architectures, we used the same techniques used for the feature extraction in the hybrid architectures. The architectures were compared in terms of accuracy, sensitivity, precision and F1-score using the Scott Knott test and the Borda count voting method. All the empirical evaluations were over three datasets: APTOS, Kaggle DR and Messidor-2, using a k-fold cross validation method. The results showed the potential of combining deep learning techniques for feature extraction and classical machine learning techniques to classify referable diabetic retinopathy. The hybrid architecture using the SVM classifier and MobileNet_V2 for feature extraction was the top performing architecture and it was classified with the best performing end-to-end architectures in the best clusters of APTOS, Kaggle DR and Messidor-2 datasets with an accuracy equal to 88.80\%, 84.01\% and 84.05\% respectively. Note that the two end-to-end architectures DenseNet201 and MobileNet_V2 outperformed all the hybrid architectures over the three datasets. However, we recommend the use of the hybrid architecture designed with SVM and MobileNet_V2 since it is promising and less time consuming, and requires less parameter tuning compared to the end-to-end techniques. {\textcopyright} 2022 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Classification (of information), Computer aided diagnosis, Deep learning, Diabetic retinopathy, Empirical evaluations, End to end, Extraction, Eye protection, Feature extraction, Features extraction, Hybrid architectures, Image classification, Learning algorithms, Learning architectures, Learning techniques, Medical image, Medical imaging, Performance, Support vector machines, Vision impairments},
	doi = {10.1080/21681163.2022.2060864},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129262042\&doi=10.1080\%2f21681163.2022.2060864\&partnerID=40\&md5=35c1d7e5976fbdb00e23b4aa456eadc9},
	author = {Lahmar, C. and Idri, A.}
}
@article {ElAlaoui2022435,
	title = {Deep Stacked Ensemble for Breast Cancer Diagnosis},
	journal = {Lecture Notes in Networks and Systems},
	volume = {468 LNNS},
	year = {2022},
	note = {cited By 1},
	pages = {435-445},
	abstract = {Breast cancer is considered one of the major public health issues and a leading cause of death among women in the world. Its early diagnosis can significantly help to increase the chances of survival rate. Therefore, this study proposes a deep stacking ensemble technique for binary classification of breast histopathological images over the BreakHis dataset. Initially, to form the base learners of the deep stacking ensemble, we trained seven deep learning (DL) techniques based on pre-trained VGG16, VGG19, ResNet50, Inception_V3, Inception_ResNet_V2, Xception, and MobileNet with a 5-fold cross-validation method. Then, a meta-model was built, a logistic regression algorithm that learns how to best combine the predictions of the base learners. Furthermore, to evaluate and compare the performance of the proposed technique, we used: (1) four classification performance criteria (accuracy, precision, recall, and F1-score), and (2) Scott Knott (SK) statistical test to cluster and identify the outperforming models. Results showed the potential of the stacked deep learning techniques to classify breast cancer images into malignant or benign tumor. The proposed deep stacking ensemble reports an overall accuracy of 93.8\%, 93.0\%, 93.3\%, and 91.8\% over the four magnification factors (MF) values of the BreakHis dataset: 40X, 100X, 200X and 400X, respectively. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04826-5_44},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130273664\&doi=10.1007\%2f978-3-031-04826-5_44\&partnerID=40\&md5=4246b5750ce0b8a03e4a80001cdfc5e5},
	author = {El Alaoui, O. and Zerouaoui, H. and Idri, A.}
}
@article {Benbriqa2021588,
	title = {Deep and Ensemble Learning Based Land Use and Land Cover Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12951 LNCS},
	year = {2021},
	note = {cited By 1},
	pages = {588-604},
	abstract = {Monitoring of Land use and Land cover (LULC) changes is a highly encumbering task for humans. Therefore, machine learning based classification systems can help to deal with this challenge. In this context, this study evaluates and compares the performance of two Single Learning (SL) techniques and one Ensemble Learning (EL) technique. All the empirical evaluations were over the open source LULC dataset proposed by the German Center for Artificial Intelligence (EuroSAT), and used the performance criteria -accuracy, precision, recall, F1 score and change in accuracy for the EL classifiers-. We firstly evaluate the performance of SL techniques: Building and optimizing a Convolutional Neural Network architecture, implementing Transfer learning, and training Machine learning algorithms on visual features extracted by Deep Feature Extractors. Second, we assess EL techniques and compare them with SL classifiers. Finally, we compare the capability of EL and hyperparameter tuning to improve the performance of the Deep Learning models we built. These experiments showed that Transfer learning is the SL technique that achieves the highest accuracy and that EL can indeed outperform the SL classifiers. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Classification (of information), Convolutional neural networks, Deep feature extraction, Deep learning, Ensemble learning, Features extraction, Hyper-parameter optimizations, Land cover, Land use, Learning algorithms, Learning classifiers, Learning techniques, Network architecture, Performance, Transfer learning},
	doi = {10.1007/978-3-030-86970-0_41},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115689890\&doi=10.1007\%2f978-3-030-86970-0_41\&partnerID=40\&md5=910871e0f58b4f00460e5e2509929a23},
	author = {Benbriqa, H. and Abnane, I. and Idri, A. and Tabiti, K.}
}
@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.}
}
@article {Chlioui2020547,
	title = {Data preprocessing in knowledge discovery in breast cancer: systematic mapping study},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	volume = {8},
	number = {5},
	year = {2020},
	note = {cited By 5},
	pages = {547-561},
	abstract = {Data Mining (DM) is a set of techniques that allow to analyse data from different perspectives and summarising it into useful information. Data mining has been increasingly used in medicine, especially in oncology. Data preprocessing is the most important step of knowledge extraction process and allows to improve the performance of the DM models. Breast cancer (BC) becomes the most common cancer among females worldwide and the leading cause of women{\textquoteright}s death. This paper aims to perform a systematic mapping study to analyse and synthesise studies on the application of preprocessing techniques for a DM task in breast cancer.Therefore, 66 relevant articles published between 2000 and October 2018 were selected and analysed according to five criteria: year/channel of publication, research type, medical task, empirical type and preprocessing task. The results show that Conferences and journals are the most targeted publication sources, researchers were more interested in applying preprocessing techniques for the diagnosis of BC, historical-based evaluation was the most used empirical type in the evaluation of preprocessing techniques in BC, and data reduction was the most investigated task of preprocessing in BC. However, A low number of papers discussed treatment which encourages researchers to devote more efforts to this task. {\textcopyright} 2020 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {algorithm, Article, Breast Cancer, cancer classification, cancer prognosis, clinical assessment, clinical outcome, Data mining, Data mining models, Data mining tasks, Data preprocessing, Diagnosis, diagnostic accuracy, Diseases, Extraction process, health promotion, human, image analysis, knowledge, knowledge discovery, Knowledge extraction, Machine learning, Mapping, Medical informatics, nerve cell network, neural crest cell, Performance, Pre-processing techniques, processing, screening test, Systematic mapping studies, Systematic Review, validity},
	doi = {10.1080/21681163.2020.1730974},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080112312\&doi=10.1080\%2f21681163.2020.1730974\&partnerID=40\&md5=befb1bc3f31f676a8e95bbc5bff5ab6d},
	author = {Chlioui, I. and Idri, A. and Abnane, I.}
}
@article {ElIdrissi2020379,
	title = {Deep Learning for Blood Glucose Prediction: CNN vs LSTM},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12250 LNCS},
	year = {2020},
	note = {cited By 6},
	pages = {379-393},
	abstract = {To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this study, we propose a Convolutional Neural Network (CNN) for BGL prediction. This CNN is compared with Long-short-term memory (LSTM) model for both one-step and multi-steps prediction. The objectives of this work are: 1) Determining the best configuration of the proposed CNN, 2) Determining the best strategy of multi-steps forecasting (MSF) using the obtained CNN for a prediction horizon of 30~min, and 3) Comparing the CNN and LSTM models for one-step and multi-steps prediction. Toward the first objective, we conducted series of experiments through parameter selection. Then five MSF strategies are developed for the CNN to reach the second objective. Finally, for the third objective, comparisons between CNN and LSTM models are conducted and assessed by the Wilcoxon statistical test. All the experiments were conducted using 10 patients{\textquoteright} datasets and the performance is evaluated through the Root Mean Square Error. The results show that the proposed CNN outperformed significantly the LSTM model for both one-step and multi-steps prediction and no MSF strategy outperforms the others for CNN. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Blood, Blood glucose, Blood glucose level, Convolutional neural networks, Deep learning, Diabetic patient, Disease control, Forecasting, Glucose, Long short-term memory, Mean square error, Multi-step, Parameter selection, Prediction horizon, Root mean square errors},
	doi = {10.1007/978-3-030-58802-1_28},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093089262\&doi=10.1007\%2f978-3-030-58802-1_28\&partnerID=40\&md5=f16d576a801dcdbbac35f8593e4a50ea},
	author = {El Idrissi, T. and Idri, A.}
}
@article {ElIdrissi20181142,
	title = {Data mining techniques in diabetes self-management: A systematic map},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {746},
	year = {2018},
	pages = {1142-1152},
	doi = {10.1007/978-3-319-77712-2_109},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045346296\&doi=10.1007\%2f978-3-319-77712-2_109\&partnerID=40\&md5=627778cf3f0df5fe749b6e457a0eef1f},
	author = {El Idrissi, T. and Idri, A. and Bakkoury, Z.}
}
@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.}
}
@conference {Idri2017245,
	title = {A Data Mining-Based Approach for Cardiovascular Dysautonomias Diagnosis and Treatment},
	booktitle = {IEEE CIT 2017 - 17th IEEE International Conference on Computer and Information Technology},
	year = {2017},
	note = {cited By 0},
	pages = {245-252},
	abstract = {Autonomic nervous system (ANS) is a control system that acts largely unconsciously and regulates bodily functions. An autonomic malfunction can lead to serious problems related to blood pressure, heart, swallowing, breathing and others. A set of dynamic tests are therefore adopted in ANS units to diagnose and treat patients with cardiovascular dysautonomias. These tests generate big amount of data which are very well suited to be processed using data mining techniques. The purpose of this study is to develop a cardiovascular dysautonomias prediction system to identify the appropriate diagnosis and treatment for patients with cardiovascular dysautonomias using a dataset extracted from the ANS unit of the university hospital Avicenne in Morocco. Classification techniques and association rules were used for the diagnosis and treatment stages respectively. In fact, K-nearest neighbors, C4.5 decision tree algorithm, Random forest, Na{\"\i}ve bayes and Support vector machine were applied to generate the diagnosis classification models and Apriori algorithm was used for generating the association rules. The results obtained for each classifier were analyzed and compared to identify the most efficient one. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/CIT.2017.28},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032340028\&doi=10.1109\%2fCIT.2017.28\&partnerID=40\&md5=8c8a9ba277b01051d8429283af6bb318},
	author = {Idri, A. and Kadi, I.}
}
@conference {Kadi2015816,
	title = {A decision tree-based approach for cardiovascular dysautonomias diagnosis: A case study},
	booktitle = {Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015},
	year = {2015},
	note = {cited By 1},
	pages = {816-823},
	abstract = {Terms as knowledge Discovery from Databases (KDD), Data Mining (DM) and Machine Learning (ML), gain from day to day, an increasing significance in medical data analysis. They permit the identification, evaluation, and quantification of some less visible, intuitively unpredictable, by using generally large sets of data. Researchers have long been concerned with applying DM tools to improve data analysis on large data sets. DM has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology. Autonomic nervous system (ANS) is the part of the nervous system that is involved in homeostasis of the whole body functions. A malfunction in this system can lead to a cardiovascular dysautonomias. Thereby, a set of dynamic tests are adopted in ANS units to diagnose and treat patients with cardiovascular dysautonomias. In this paper, a case study was performed in order to construct a cardiovascular dysautonomias prediction system using data mining techniques and a dataset collected from an ANS unit of the Moroccan university hospital Avicenne. The prediction system is a decision tree-based classifier that was developed using C4.5 decision tree algorithm to automate the analysis procedure of ANS{\textquoteright}s test results and make it easier for specialists. The performance of the generated decision trees was evaluated and the results obtained achieved high accuracy rates which were very promising. In addition, a clinical validation of the developed system was carried out on new patients. In fact, a prototype of the developed system was implemented on JEE platform and deployed in the ANS unit so as to be validated clinically. The results were analyzed and thus the prototype was approved to be highly accurate, interpretable, time saving and easy to use. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/SSCI.2015.121},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964940639\&doi=10.1109\%2fSSCI.2015.121\&partnerID=40\&md5=f0d227a9f5e18f21b2c5aebad30b41f8},
	author = {Kadi, I. and Idri, A.}
}
