Heart Disease Data Classification using Data Mining Techniques
LAP Lambert Academic Publishing
Published on 25. March 2019
Book
Paperback/Softback
248 pages
978-613-9-46428-9 (ISBN)
Description
This book is an essential reference work for academicians, professionals, researchers, and students interested in the relationship between data mining and medical database. This book offers an overview of machine learning technologies and evolutionary techniques in decision support systems for the diagnosis of heart disease based on medical data. Data collected from various hospitals were selected and preprocessed for this study. These techniques are used to explore risk factors associated with heart disease. This book also covers state-of-the-art research toward developing a decision support system for heart disease prediction with machine learning approaches. The book presents numerous techniques, algorithms, and models. It describes neural networks, evolutionary techniques like genetic algorithms, associative classification, and statistical models, and machine learning approaches for heart disease prediction. We discussed how these machine learning techniques are used to classify heart disease data sets.
More details
Language
English
Dimensions
Height: 220 mm
Width: 150 mm
Thickness: 16 mm
Weight
387 gr
ISBN-13
978-613-9-46428-9 (9786139464289)
Schweitzer Classification
Persons
Dr. M.A.JABBAR is a Professor, Dept of CSE at, Vardhaman College of Engineering, Hyderabad, India. He has been teaching for more than 19 years. He obtained his Ph.D. from JNTUH. He is serving as a vice chair of the IEEE Computer Society chapter (Hyderabad) Section. He received the best faculty researcher award from CSI and Fossee labs Mumbai.