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Data Science and Predictive Analytics Biomedical and Health Applications using R, ISBN-13: 978-3319723464
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"Data Science and Predictive Analytics: Biomedical and Health Applications using R" explores the application of data science and predictive analytics in the biomedical and healthcare fields, emphasizing the use of the R programming language for practical implementation. The book provides an in-depth examination of various statistical methods and machine learning techniques, illustrated with real-world healthcare data examples. It aims to equip readers with the necessary tools to analyze complex health-related datasets, identify trends, and make informed predictions, ultimately improving clinical decision-making and patient outcomes.
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Title: Data Science and Predictive Analytics: Biomedical and Health Applications using R
Authors: +Steven N. Goodman, Danny L. B. Kauffman, and John P. A. Ioannidis
Publisher: Springer
Publication Year: 2018
ISBN-13: 978-3319723464
Series: Statistics for Biology and Health
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"Data Science and Predictive Analytics: Biomedical and Health Applications using R" serves as a comprehensive guide for leveraging R in the field of biomedical data analysis. By integrating theoretical concepts with practical applications, the book equips readers with essential skills to tackle complex health-related challenges. Its focus on real-world case studies and hands-on coding examples makes it an invaluable resource for researchers and practitioners alike. The integration of predictive analytics into healthcare highlights the transformative potential of data-driven approaches, ultimately paving the way for enhanced decision-making and improved patient outcomes.
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PDF – ePub: Data Science and Predictive Analytics Biomedical and Health Applications using R, ISBN-1
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