Healthcare Analytics Made Simple: Techniques in healthcare computing using machine learning and Python
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About this item
"In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists' work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples"--
ASIN: 1787286703
VSKU: DBV.1787286703.A
Condition: Acceptable
Author/Artist:Kumar, Vikas
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: This copy has clearly been enjoyed—expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
ASIN: 1787286703
VSKU: DBV.1787286703.A
Condition: Acceptable
Author/Artist:Kumar, Vikas
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: This copy has clearly been enjoyed—expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
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