@conference {ElIdrissi2020337,
	title = {Strategies of multi-step-ahead forecasting for blood glucose level using LSTM neural networks: A comparative study},
	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 3},
	pages = {337-344},
	abstract = {Predicting the blood glucose level (BGL) is crucial for self-management of Diabetes. In general, a BGL prediction is done based on the previous measurements of BGL, which can be taken either (manually) by using sticks or (automatically) by using continuous glucose monitoring (CGM) devices. To allow the diabetic patients to take appropriate actions, the BGL predictions should be done ahead of time; thus a multi-step ahead prediction is suitable. Therefore, many Multi-Step-ahead Forecasting (MSF) strategies have been developed and evaluated, and can be categorized in five types: Recursive, Direct, MIMO (for Multiple Input Multiple Output), DirMO (combining Direct and MIMO) and DirRec (combining Direct and Recursive). However, none of them is known to be the best strategy in all contexts. The present study aims at: 1) reviewing the MSF strategies, and 2) determining the best strategy to fit with a LSTM Neural Network model. Hence, we evaluated and compared in terms of two performance criteria: Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE), the five MSF strategies using a LSTM Neural Network with an horizon of 30 minutes. The results show that there is no strategy that significantly outperformed others when using the Wilcoxon statistical test. However, when using the Sum Ranking Differences method, MIMO is the best strategy for both RMSE and MAE criteria. {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Biomedical engineering, Blood, Blood glucose level, Comparative studies, Continuous glucosemonitoring (CGM), Forecasting, Glucose, Long short-term memory, Mean absolute error, Mean square error, Medical informatics, MIMO systems, Multi-step-ahead predictions, Neural network model, Performance criterion, Root mean square errors},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083722214\&partnerID=40\&md5=bd9e45a8538559492b02432e5567d10e},
	author = {El Idrissi, T. and Idri, A. and Kadi, I. and Bakkoury, Z.}
}
@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.}
}
@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.}
}
@article {Kadi201712,
	title = {Knowledge discovery in cardiology: A systematic literature review},
	journal = {International Journal of Medical Informatics},
	volume = {97},
	year = {2017},
	note = {cited By 6},
	pages = {12-32},
	abstract = {Context Data mining (DM) provides the methodology and technology needed to transform huge amounts of data into useful information for decision making. It is a powerful process employed to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, DM applications can greatly benefit all those involved in cardiology, such as patients, cardiologists and nurses. Objective The purpose of this paper is to review papers concerning the application of DM techniques in cardiology so as to summarize and analyze evidence regarding: (1) the DM techniques most frequently used in cardiology; (2) the performance of DM models in cardiology; (3) comparisons of the performance of different DM models in cardiology. Method We performed a systematic literature review of empirical studies on the application of DM techniques in cardiology published in the period between 1 January 2000 and 31 December 2015. Results A total of 149 articles published between 2000 and 2015 were selected, studied and analyzed according to the following criteria: DM techniques and performance of the approaches developed. The results obtained showed that a significant number of the studies selected used classification and prediction techniques when developing DM models. Neural networks, decision trees and support vector machines were identified as being the techniques most frequently employed when developing DM models in cardiology. Moreover, neural networks and support vector machines achieved the highest accuracy rates and were proved to be more efficient than other techniques. {\textcopyright} 2016 Elsevier Ireland Ltd},
	doi = {10.1016/j.ijmedinf.2016.09.005},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988689033\&doi=10.1016\%2fj.ijmedinf.2016.09.005\&partnerID=40\&md5=9bd4f89bacb9b8dea94dd1f7eefef431},
	author = {Kadi, I. and Idri, A. and Fernandez-Aleman, J.L.}
}
@article { ISI:000390485500002,
	title = {Knowledge discovery in cardiology: A systematic literature review},
	journal = {INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS},
	volume = {97},
	year = {2017},
	month = {JAN},
	pages = {12-32},
	abstract = {Context: Data mining (DM) provides the methodology and technology needed to transform huge amounts of data into useful information for decision making. It is a powerful process employed to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, DM applications can greatly benefit all those involved in cardiology, such as patients, cardiologists and nurses. Objective: The purpose of this paper is to review papers concerning the application of DM techniques in cardiology so as to summarize and analyze evidence regarding: (1) the DM techniques most frequently used in cardiology; (2) the performance of DM models in cardiology; (3) comparisons of the performance of different DM models in cardiology. Method: We performed a systematic literature review of empirical studies on the application of DM techniques in cardiology published in the period between 1 January 2000 and 31 December 2015. Results: A total of 149 articles published between 2000 and 2015 were selected, studied and analyzed according to the following criteria: DM techniques and performance of the approaches developed. The results obtained showed that a significant number of the studies selected used classification and prediction techniques when developing DM models. Neural networks, decision trees and support vector machines were identified as being the techniques most frequently employed when developing DM models in cardiology. Moreover, neural networks and support vector machines achieved the highest accuracy rates and were proved to be more efficient than other techniques. (C) 2016 Elsevier Ireland Ltd. All rights reserved.},
	issn = {1386-5056},
	doi = {10.1016/j.ijmedinf.2016.09.005},
	author = {Kadi, I. and Idri, A. and Fernandez-Aleman, J. L.}
}
@conference {Kadi2017,
	title = {Quality evaluation of cardiac decision support systems using ISO 25010 standard},
	booktitle = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA},
	year = {2017},
	note = {cited By 0},
	abstract = {Clinical decision support system (CDSS) is a technology designed to assist physicians and medical stuff with clinical decision-making tasks. It aims to better the safety and quality of patient care, improve patient care treatments and outcomes, decrease the dependence on memory, lower error rates, and decrease response time. CDSS is not intended to replace clinician judgment, but it supports users with the detection and prevention of possible risks to patient safety and encouraging the appropriate usage of evidence-based practice and guidelines. The benefits of the CDSSs contribute in increasing the number of e-health applications in recent years. Cardiology healthcare institutions are adopting such systems in their departments in order to assist cardiologists when making their decisions. This study proposes a set of cardiac DSS requirements and measures the influence of these requirements on software product quality using the ISO/IEC 25010 software quality model. A checklist of cardiac DSS requirements is also presented. As a result, Functional suitability, Reliability, Performance efficiency and Operability external characteristics were identified as the most influenced by cardiac DSS requirements. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/AICCSA.2016.7945657},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021912425\&doi=10.1109\%2fAICCSA.2016.7945657\&partnerID=40\&md5=92b7b2292f1248fc9c7033b5d5bfe20e},
	author = {Kadi, I. and Idri, A. and Ouhbi, S.}
}
@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.}
}
@article {Idri20161,
	title = {Evaluating a decision making system for cardiovascular dysautonomias diagnosis},
	journal = {SpringerPlus},
	volume = {5},
	number = {1},
	year = {2016},
	note = {cited By 1},
	pages = {1-17},
	abstract = {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. Hence, a set of dynamic tests are adopted in ANS units to diagnose and treat patients with cardiovascular dysautonomias. The purpose of this study is to develop and evaluate a decision tree based cardiovascular dysautonomias prediction system on a dataset collected from the ANS unit of the Moroccan university hospital Avicenne. We collected a dataset of 263 records from the ANS unit of the Avicenne hospital. This dataset was split into three subsets: training set (123 records), test set (55 records) and validation set (85 records). C4.5 decision tree algorithm was used in this study to develop the prediction system. Moreover, Java Enterprise Edition platform was used to implement a prototype of the developed system which was deployed in the Avicenne ANS unit so as to be clinically validated. The performance of the decision tree-based prediction system was evaluated by means of the error rate criterion. The error rates were measured for each classifier and have achieved an average value of 1.46, 2.24 and 0.89~\% in training, test, and validation sets respectively. The results obtained were encouraging but further replicated studies are still needed to be performed in order to confirm the findings of this study. {\textcopyright} 2016, Idri and Kadi.},
	doi = {10.1186/s40064-016-1730-7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955586605\&doi=10.1186\%2fs40064-016-1730-7\&partnerID=40\&md5=0c9525bf9e5c6b03de358df01c579172},
	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.}
}
