Novel Method for Heart Disease Prediction Using Feature Selection Method and Classification Algorithms
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Novel Method for Heart Disease Prediction Using Feature Selection Method and Classification Algorithms

Novel Method for Heart Disease Prediction Using Feature Selection Method and Classification Algorithms


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About the Book

The groundbreaking book "A Novel Method for Heart Disease Prediction Using Feature Selection Method and Classification Algorithms" by author C. Sowmiya offers a thorough examination of the most recent techniques for heart disease prediction. The use of feature selection strategies and classification algorithms to precisely identify people at risk of heart disease is covered in depth in this book. The author has created a potent tool for medical practitioners by skillfully fusing the most recent developments in computational intelligence and machine learning.


In order to make the book understandable for a broad audience of readers, C. Sowmya provides simple and succinct explanations of difficult concepts and approaches throughout. To ensure that readers have a thorough understanding of the subject, the author discusses a range of various algorithms and methodologies, such as decision trees, support vector machines, and artificial neural networks.


For medical professionals, researchers, and students who want to learn more about the most recent advancements in cardiac disease prediction, this book is a must-have resource. "A Novel Method for Heart Disease Prediction Using Feature Selection Method and Classification Algorithms" is a great resource for anyone wishing to learn more about this fast expanding topic because of its pragmatic approach and simple-to-follow directions.


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Product Details
  • ISBN-13: 9789183128626
  • Publisher: Independent Author
  • Publisher Imprint: Independent Author
  • Height: 229 mm
  • No of Pages: 126
  • Spine Width: 7 mm
  • Width: 152 mm
  • ISBN-10: 918312862X
  • Publisher Date: 05 Feb 2023
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Weight: 227 gr


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