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Predicting Breast Cancer Survivability Using Data Mining Techniques: Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression - Softcover
Inhaltsangabe
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.
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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.
Biografía del autor
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.
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Predicting Breast Cancer Survivability Using Data Mining Techniques
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846538787
ISBN 13: 9783846538784
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Hussain OmeadI 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 Stat. Bestandsnummer des Verkäufers 5497618
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Predicting Breast Cancer Survivability Using Data Mining Techniques
Verlag:
LAP LAMBERT Academic Publishing Okt 2011, 2011
ISBN 10: 3846538787
ISBN 13: 9783846538784
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 136 pp. Englisch. Bestandsnummer des Verkäufers 9783846538784
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Predicting Breast Cancer Survivability Using Data Mining Techniques : Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846538787
ISBN 13: 9783846538784
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9783846538784
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Predicting Breast Cancer Survivability Using Data Mining Techniques
Verlag:
LAP LAMBERT Academic Publishing Okt 2011, 2011
ISBN 10: 3846538787
ISBN 13: 9783846538784
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 136 pp. Englisch. Bestandsnummer des Verkäufers 9783846538784
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Predicting Breast Cancer Survivability Using Data Mining Techniques: Comparing between three modeling tools which are : Artificial neural network, decision trees and logistic regression
Verlag:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846538787
ISBN 13: 9783846538784
Neu
Paperback
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Paperback. Zustand: Brand New. 136 pages. 8.66x5.91x0.31 inches. In Stock. Bestandsnummer des Verkäufers 3846538787
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