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Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

By : Kumar, Khader
4.4 (8)
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Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

4.4 (8)
By: Kumar, Khader

Overview of this book

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.
Table of Contents (11 chapters)
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Computing Foundations – Introduction to Python

This chapter will provide an introduction to Python for analytics. It is meant mainly for novice programmers or developers who are not familiar with Python. By the end of the chapter, you will have a basic familiarity with the features of the Python base language, which is integral for healthcare analytics and machine learning. You will also understand how to get started using pandas and scikit-learn, two important Python libraries for analytics.

If you would like to follow along using the Jupyter Notebook, we encourage you to refer to the directions in Chapter 1, Introduction to Healthcare Analytics, to start a new Jupyter session. The notebook for this chapter is also available online at the book's official code repository.

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