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)

ASD screening using machine learning

We will be using a Jupyter Notebook, which runs on iPython. We will also use the pandas, Keras, and scikit-learn libraries here:

  1. To open a Notebook we will use the Command Prompt in Windows.
  2. We will first navigate to the directory where our project is present using the cd command, as shown in the following screenshot:
  1. Once we are in the required directory, we will open up Jupyter Lab using the following command:
jupyter lab
  1. When we press Enter after this command, we will see the Notebook open. Here, we will see that there's an untitled file open in the Notebook. We will then rename that file to autism_detection.
Ctrl + B will close the directory window present on the left side. This will expand your Notebook so that it takes up the whole screen.