Boosted Decision Trees for Lithiasis Type Identification
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2015, Vol 6, Issue 6
Abstract
Several urologic studies showed that it was important to determine the lithiasis types, in order to limit the recurrence residive risk and the renal function deterioration. The difficult problem posed by urologists for classifying urolithiasis is due to the large number of parameters (components, age, gender, background ...) taking part in the classification, and hence the probable etiology determination. There exist 6 types of urinary lithiasis which are distinguished according to their compositions (chemical components with given proportions), their etiologies and patient profile. This work presents models based on Boosted decision trees results, and which were compared according to their error rates and the runtime. The principal objectives of this work are intended to facilitate the urinary lithiasis classification, to reduce the classification runtime and an epidemiologic interest. The experimental results showed that the method is effective and encouraging for the lithiasis type identification.
Authors and Affiliations
Boutalbi Rafika, Farah Nadir, Chitibi Eddine, Boutefnouchet Boutefnouchet, Tanougast Camel
An Interoperable Data Framework to Manipulate the Smart City Data using Semantic Technologies
During the last decade, enormous volumes of urban data have been produced by the Government agencies, the NGOs and the citizens. In such a scenario, we are presented with a diverse sets of data which holds valuable infor...
Image Transmission Model with Quality of Service and Energy Economy in Wireless Multimedia Sensor Network
The objective of this article is to present the efficiency of image compression in the Wireless Multimedia Sensor Network (WMSN), the method used in this work is based on the lifting scheme coupled with the SPIHT coding...
Review of Community Detection over Social Media:Graph Prospective
Community over the social media is the group of globally distributed end users having similar attitude towards a particular topic or product. Community detection algorithm is used to identify the social atoms that are mo...
SAS: Implementation of scaled association rules on spatial multidimensional quantitative dataset
Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining (SDM). Because of the complexity of spatial data, a traditional method in extracting spatial association rules is...
An Optimal Load Balanced Resource Allocation Scheme for Heterogeneous Wireless Networks based on Big Data Technology
An important issue in heterogeneous wireless networks is how to optimally utilize various radio resources. While many methods have been proposed for managing radio resources in each network, these methods are not suitabl...