Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/20836
Title: | Automatic Recognition of Descriptors helping to Cause Diabetes in Algeria |
Authors: | LAZOUNI, Mohammed El Amine DAHO, Mostafa EL HABIB MESSAIDI, Mahammed |
Keywords: | Diabetes Type2 Feature Selection Method Database Support Vector Machine Random Forest Classification And Regression Tree K-Nearest Neighbor Multilayer Perceptron Majority Voting System |
Issue Date: | 4-Mar-2019 |
Publisher: | Université Kasdi Merbah Ouargla |
Series/Report no.: | 2019; |
Abstract: | The computer aided medical diagnosis systems can use a great number of very important medical data in order to help doctors in detecting different pathologies. We assume that the grater data we have, the more we facilitate and ameliorate the quality of classification. However, the classification quality does not directly depend on the size of the available database but it rather depends on its pertinence. For this, the purpose of this paper is to two different problems. The first one is the selection of the pertinent descriptors that help causing diabetes using a Random Forest feature selection approach. The second is the combination of several different machines learning algorithms (Support Vector Machine (SVM), K-Nearest Neighbor (KNN), the Multilayer Perceptron (MLP) and two Decision tree based classifiers (Classification And Regression Tree (CART), and Random Forests) in order to classify type 2 diabetic patients. We used also a majority voting method between the proposed five classifiers. In our paper, we selected an experimental database composed of 625 patients, each of whom being represented by 31 descriptors. These patients were selected in various private clinics and hospitals in western Algeria. |
Description: | Le 2eme Conference Internationale sur intelligence Artificielle et les Technologies Information ICAIIT 2019 |
URI: | http://dspace.univ-ouargla.dz/jspui/handle/123456789/20836 |
Appears in Collections: | 2. Faculté des nouvelles technologies de l’information et de la communication |
Files in This Item:
File | Description | Size | Format | |
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Mohammed El Amine LAZOUNI.pdf | 612,61 kB | Adobe PDF | View/Open |
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