Book Image

Healthcare Analytics Made Simple

By : Vikas (Vik) Kumar, Shameer Khader
Book Image

Healthcare Analytics Made Simple

By: Vikas (Vik) Kumar, Shameer 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)

Final preprocessing steps

Now that we have gone through all of the variable groups, we are almost ready to build our predictive models. But first, we must expand all of our categorical variables into binary variables (also known as one-hot encoding or a 1-of-K representation) and convert our data into a format suitable for input into the scikit-learn methods. Let's do that next.

One-hot encoding

Many classifiers of the scikit-learn library require categorical variables to be one-hot encoded. One-hot encoding, or a 1-of-K representation, is when a categorical variable that has more than two possible values is recorded as multiple variables each having two possible values.

For example, let's say that we have five patients...