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Incomplete Data Handling in Medical Informatics: - Data Mining Approaches - Tapa blanda

Ilango, P.; Thiagarajan, Hemalatha

 
9783659112003: Incomplete Data Handling in Medical Informatics: - Data Mining Approaches
Tapa blanda
ISBN 10: 3659112003 ISBN 13: 9783659112003
Editorial: LAP LAMBERT Academic Publishing, 2012

Sinopsis

The recent advancement in databases, data mining and data warehousing have promoted edge over technologies to acquire, represent, process, and manage data and knowledge related to health and healthcare in Medical Informatics. The innovations in computer assisted approaches address the challenges in several real life diagnostic and prognostic studies and expert systems. Data Mining, Machine Learning, and Artificial Neural Network are some of the widely used techniques in Medical Informatics to derive useful knowledge over the patient records which enhance the quality of diagnosis and effectiveness of the treatment. The performance of the data mining algorithms solely depends on the nature and quality of the sample taken as training dataset. The large quantities of cumulative data collected from various OLTP sources are not commensurate with either the structure or quality. Various qualitative deficiency factors such as incompleteness, inconsistency redundancy, and noise usually envelop the training data. In this book, various methods for the imputation of missing data are discussed, the performance are also evaluated and compared with the other existing methods

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Reseña del editor

The recent advancement in databases, data mining and data warehousing have promoted edge over technologies to acquire, represent, process, and manage data and knowledge related to health and healthcare in Medical Informatics. The innovations in computer assisted approaches address the challenges in several real life diagnostic and prognostic studies and expert systems. Data Mining, Machine Learning, and Artificial Neural Network are some of the widely used techniques in Medical Informatics to derive useful knowledge over the patient records which enhance the quality of diagnosis and effectiveness of the treatment. The performance of the data mining algorithms solely depends on the nature and quality of the sample taken as training dataset. The large quantities of cumulative data collected from various OLTP sources are not commensurate with either the structure or quality. Various qualitative deficiency factors such as incompleteness, inconsistency redundancy, and noise usually envelop the training data. In this book, various methods for the imputation of missing data are discussed, the performance are also evaluated and compared with the other existing methods

Biografía del autor

Dr.P.ILANGO received Ph.D from NIT,Trichy in 2009. He is a Professor,School of Computing Sc & Engg,VIT University,Vellore. He focuses on Data Mining & Warehousing and Intelligent Systems. Dr.T.Hemalatha,Professor,Dept.of Computer Applications, NIT,Tricy. She enjoys motivating,advising and guiding her research scholars pursuing Ph.D Degree.

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Editorial
LAP LAMBERT Academic Publishing
Año de publicación
2012
Idioma
Inglés
ISBN 10 
3659112003
ISBN 13 
9783659112003
Encuadernación
Tapa blanda
Número de páginas
116
Contacto del fabricante
no disponible
Persona responsable
no disponible