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)

Building our Keras model

We'll now start building our Keras model, which is a deep learning algorithm:

  1. The first thing that we're going to do is import the necessary packages and layers. We will do that by running the following lines of code:
from sklearn.model_selection import GridSearchCV, KFold
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.optimizers import Adam

In the preceding code snippet , GridSearchCV is the function we will use to perform a grid search, and KFold will be used for performing the k-fold cross-validation. KerasClassifier is used as the wrapper. Adam is the optimizer that we will be using for this model; the rest of the functions are all general functions used to define the model:

  1. Let's start by defining the model. We will use a create_model() function, because...