Book Image

Machine Learning for Healthcare Analytics Projects

Book Image

Machine Learning for Healthcare Analytics Projects

Overview of this book

Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain.
Table of Contents (7 chapters)

The dataset

To begin with, let's open Command Prompt and execute the following command:

cd tutorial
jupyter lab

This will take us to the tutorial folder. From here, we can open up JupyterLab. This folder is going to be empty right now, but it is where we will be completing this tutorial.

The dataset we're going to use is the heart disease dataset from the UCI repository. You can download this from archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/. It has around 303 patients collected from the Cleveland Clinic Foundation. They have also added data from other places as well, but we are only going to look at data from Cleveland for now. If you go over to the Data folder, you'll see that we've got lot's of different options:

Even if you don't go to the preceding URL, we can directly import all of the files present there directly into our...