Omead Hussain

Predicting Breast Cancer Survivability Using Data Mining Techniques

Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression

• Broschiertes Buch

Produktbeschreibung

This book concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods, and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we found the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques.

Produktdetails

• Verlag: LAP LAMBERT Academic Publishing
• Seitenzahl: 136
• Erscheinungstermin: 20. Oktober 2011
• Englisch
• Abmessung: 220mm x 150mm x 9mm
• Gewicht: 221g
• ISBN-13: 9783846538784
• ISBN-10: 3846538787
• Artikelnr.: 34455590

Autorenporträt

I have got two Masters Degrees, the first one in (M.Sc. Internet and database systems) from the Business Computing and Information Management Faculty/London South Bank University, 2009.The second Master in (M.Sc. in Statistical Science) from University of Al-Mustansiriya, Baghdad, Iraq, 1992.For 12 years I¿ve worked as a University lecturer.