@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.}
}
