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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
- Veröffentlichungsjahr: 2018
Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn
Key Features
- Develop a range of healthcare analytics projects using real-world datasets
- Implement key machine learning algorithms using a range of libraries from the Python ecosystem
- Accomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologies
Book Description
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.
What you will learn
- Explore super imaging and natural language processing (NLP) to classify DNA sequencing
- Detect cancer based on the cell information provided to the SVM
- Apply supervised learning techniques to diagnose autism spectrum disorder (ASD)
- Implement a deep learning grid and deep neural networks for detecting diabetes
- Analyze data from blood pressure, heart rate, and cholesterol level tests using neural networks
- Use ML algorithms to detect autistic disorders
Who this book is for
Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.
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Machine Learning for Healthcare Analytics Projects
Copyright © 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Sunith ShettyAcquisition Editor: Shweta PantContent Development Editor: Nathanya DiasTechnical Editor: Utkarsha KadamCopy Editor: Safis EditingProject Coordinator: Kirti PisatProofreader: Safis EditingIndexer: Priyanka DhadkeGraphics: Jisha ChirayilProduction Coordinator: Nilesh Mohite
First published: October 2018
Production reference: 1261018
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78953-659-1
www.packtpub.com
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About the author
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Table of Contents
Title Page
Copyright and Credits
Machine Learning for Healthcare Analytics Projects
Packt Upsell
Why subscribe?
Packt.com
Contributor
About the author
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Breast Cancer Detection
Objective of this project
Detecting breast cancer with SVM and KNN models
Data visualization with machine learning
Relationships between variables
Understanding machine learning algorithms 
Training models 
Predictions in machine learning
Summary
Diabetes Onset Detection
Detecting diabetes using a grid search
Introduction to the dataset
Preprocessing the dataset
Normalizing the dataset
Building our Keras model
Performing a grid search using scikit-learn
Reducing overfitting using dropout regularization
Finding the optimal hyperparameters
Optimizing the number of neurons
Generating predictions using optimal hyperparameters
Bonus step
Summary
DNA Classification
Classifying DNA sequences
Data preprocessing
Generating a DNA sequence
Splitting the dataset
Summary
Diagnosing Coronary Artery Disease
The dataset
Fixing missing data
Splitting the dataset
Training the neural network
A comparison of categorical and binary problems
Summary
Autism Screening with Machine Learning
ASD screening using machine learning
Introducing the dataset
Importing the data and libraries
Exploring the dataset
Data preprocessing
One-hot encoding
Splitting the dataset into training and testing datasets
Building the network
Testing the network
Solving overfitting issues using dropout regularization
Summary
Another Book You May Enjoy
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Preface
Machine learning in the healthcare domain is booming because of its ability to provide accurate and stable techniques. Machine learning algorithms provide strategies to deal with a variety of structured, unstructured, and semi-structured data. This book is packed with new approaches and methodologies to create powerful solutions for healthcare analytics.This book will implement key machine learning algorithms and their use cases using a range of libraries from the Python ecosystem. We will build five end-to-end projects within the organization to evaluate the efficiency of artificial intelligence applications when carrying out simple and complex healthcare analytics tasks. Each project will help you to delve deep into newer and better ways to manage insights and handle healthcare data efficiently. We will use machine learning to detect cancer in a set of patients using the SVM and KNN models. Apart from that, we will create a deep neural network in Keras to predict the onset of diabetes on a huge dataset of patients. We will also learn how to predict heart diseases using neural networks.By the end of this book, you will learn how to address long-standing challenges, provide specialized solutions to deal with them, and carry out a range of cognitive tasks in the healthcare domain.
Who this book is for
If you are a data scientist, machine learning engineer, or a healthcare professional who wants to implement machine learning algorithms to build smart AI applications, then this is a book for you.
Basic knowledge of Python or any programming language is expected to get the most from this book.
What this book covers
Chapter 1, Breast Cancer Detection, will show you how to import data from the UCI repository. In this chapter, we will name the columns (or features) and put them into a pandas DataFrame. We will learn how to preprocess our data and remove the ID column. We will also explore the data so that we know more about it. We will also see how to create histograms (so that we can understand the distributions of the different features) and a scatterplot matrix (so that we can look for linear relationships between the variables). We will learn how to implement some testing parameters, build a KNN classifier and an SVC, and compare their results using a classification report. Finally, we will build our own cell and explore what it would take to actually get a malignant or benign classification.
Chapter 2, Diabetes Onset Detection, covers the building of a deep neural network in Keras. We will explore the optimal hyperparameters using the scikit-learn grid search. We will also learn how to optimize a network by tuning the hyperparameters. In this chapter, we will explore how to apply the network to predict the onset of diabetes in a huge dataset of patients.
Chapter 3, DNA Classification, will show how to predict the functional outcome—or a promoter/non-promoter —for a DNA sequence from E. coli bacteria with 96% accuracy. We will look at how to import data from a repository and how to convert textual inputs to numerical data. We will then learn to build and train classification algorithms and compare and contrast them using the classification report.
Chapter 4, Diagnosing Coronary Artery Disease, will show how to use sklearn and Keras, how to import data from a UCI repository using the pandas read_csv function, and how to preprocess that data. We will then learn how to describe the data and print out histograms so we know what we're working with, followed by executing a train/test split withthe model_selection function from sklearn.
Furthermore, we will also learn how to convert one-hot encoded vectors for a categorical classification, defining simple neural networks using Keras. We will look at activation functions, such as softmax, for categorical classifications with categorical_crossentropy. We will also look at training the data and how we fit our model to our training data for both categorical and binary problems. Ultimately, we will look at how to do a classification report and an accuracy score for our results.
Chapter 5, Autism Screening with Machine Learning,will show how to predict autism in patients with approximately 90% accuracy. We will also learn how to deal with categorical data; a lot of health applications are going to have categorical data and one way to address them is by using one-hot encoded vectors. Furthermore, we will learn how to reduce overfitting using dropout regularization.
To get the most out of this book
This book will help you to build real-world machine learning solutions across the healthcare vertical using NumPy, pandas, matplotlib, scikit-learn, and so on. You need not have any prior knowledge before exploring this book. You will get well versed on how exactly machine learning is implemented to evaluate the efficiency of AI apps, and how to carry out simple-to-complex healthcare analytics tasks. This is a perfect entry point packed with practical examples to carry out a range of cognitive tasks. By the end of this book, you will have learned how to address long-standing challenges in the healthcare domain, and produce solutions for dealing with them.
Download the example code files
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
Log in or register at
www.packt.com
.
Select the
SUPPORT
tab.
Click on
Code Downloads and Errata
.
Enter the name of the book in the
Search
box and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
WinRAR/7-Zip for Windows
Zipeg/iZip/UnRarX for Mac
7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-for-Healthcare-Analytics-Projects. In case there's an update to the code, it will be updated on the existing GitHub repository. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781789536591_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We will then rename that fileautism_detection."
A block of code is set as follows:
import sysimport pandas as pdimport sklearnimport kerasprint 'Python: {}'.format(sys.version)print 'Pandas: {}'.format(pd.__version__)print 'Sklearn: {}'.format(sklearn.__version__)print 'Keras: {}'.format(keras.__version__)
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
[default]exten => s,1,Dial(Zap/1|30)exten => s,2,Voicemail(u100)
exten => s,102,Voicemail(b100)
exten => i,1,Voicemail(s0)
