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Machine Learning for Healthcare Analytics Projects
Machine Learning for Healthcare Analytics Projects

Machine Learning for Healthcare Analytics Projects: Build smart AI applications using neural network methodologies across the healthcare vertical market

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Machine Learning for Healthcare Analytics Projects

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 30, 2018
Length: 134 pages
Edition : 1st
Language : English
ISBN-13 : 9781789532524
Category :

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Product Details

Publication date : Oct 30, 2018
Length: 134 pages
Edition : 1st
Language : English
ISBN-13 : 9781789532524
Category :

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Frequently bought together


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Machine Learning for Healthcare Analytics Projects
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Table of Contents

5 Chapters
Breast Cancer Detection Chevron down icon Chevron up icon
Diabetes Onset Detection Chevron down icon Chevron up icon
DNA Classification Chevron down icon Chevron up icon
Diagnosing Coronary Artery Disease Chevron down icon Chevron up icon
Autism Screening with Machine Learning Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(4 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 25%
1 star 25%
Cesar C. Jun 30, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Buen libro
Amazon Verified review Amazon
Elliott0928 Oct 28, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A useful book in excellent condition with helpful contents and hints
Amazon Verified review Amazon
Amazon Customer Apr 23, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Print quality of this book is not good.Some printing content is difficult to read.The book is very less of content and I expected more practical examples.
Amazon Verified review Amazon
Amazon Customer Apr 12, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Read chapter 1. I might be the only one that has done so. 28 pages in and already numerous screenshots that do not match the text describing them. Multiple instances of the same exact code and output being described as having different results.
Amazon Verified review Amazon
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