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)

Introduction to the dataset

Our next step is to import the Pima Indians diabetes dataset, which contains the details of about 750 patients:

  1. The dataset that we need can be found at https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv. We can import it by using the following line:
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
  1. If we navigate to the preceding URL, we can see a lot of raw information. Once we have imported the dataset, we have to define column names. We will do this using the following lines of code:
names = ['n_pregnant', 'glucose_concentration', 'blood_pressure (mm Hg)', 'skin_thickness (mm)', 'serum_insulin (mu U/ml)', 'BMI', 'pedigree_function', 'age', 'class']

As can...