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

Eduonix Learning Solutions

  • 出版商: Packt Publishing
  • 出版日期: 2018-10-29
  • 售價: $1,140
  • 貴賓價: 9.5$1,083
  • 語言: 英文
  • 頁數: 134
  • 裝訂: Paperback
  • ISBN: 1789536596
  • ISBN-13: 9781789536591
  • 相關分類: 人工智慧Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

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.

Table of Contents

  1. Breast Cancer Detection
  2. Diabetes Onset Detection
  3. DNA classification
  4. Diagnosing Coronary Artery Disease Using machine Learning
  5. Screening Children for Autistic Spectrum Disorder using machine learning

商品描述(中文翻譯)

使用NumPy、pandas、matplotlib和scikit-learn創建真實世界的機器學習解決方案

主要特點:
- 使用真實世界數據集開發一系列醫療保健分析項目
- 使用Python生態系統中的多個庫實現關鍵機器學習算法
- 通過構建智能人工智能應用程序使用神經網絡方法完成中級到複雜的任務

書籍描述:
機器學習(ML)已改變組織和個人使用數據提高系統效率的方式。ML算法使策略家能夠處理各種結構化、非結構化和半結構化數據。《機器學習在醫療保健分析項目中的應用》充滿了創建強大醫療保健分析解決方案的新方法和方法。

本書將教您如何實現關鍵機器學習算法,並通過使用Python生態系統中的多個庫的使用案例來引導您。您將建立五個端到端項目,以評估人工智能(AI)應用程序在執行從簡單到複雜的醫療保健分析任務時的效率。通過每個項目,您將獲得新的見解,然後幫助您高效處理醫療保健數據。在閱讀本書的過程中,您將使用ML使用支持向量機(SVM)和k最近鄰(KNN)模型來檢測一組患者中的癌症。在最後幾章中,您將在Keras中創建一個深度神經網絡,以預測一個龐大的患者數據集中的糖尿病發作。您還將學習如何使用神經網絡預測心臟疾病。

通過閱讀本書,您將學習如何解決長期存在的挑戰,為如何應對這些挑戰提供專業解決方案,並在醫療領域執行一系列認知任務。

您將學到什麼:
- 探索超級成像和自然語言處理(NLP)以對DNA序列進行分類
- 基於提供給SVM的細胞信息檢測癌症
- 應用監督學習技術診斷自閉症譜系障礙(ASD)
- 使用深度學習網格和深度神經網絡檢測糖尿病
- 使用神經網絡分析血壓、心率和膽固醇水平測試數據
- 使用ML算法檢測自閉症譜系障礙

本書適合對象:
《機器學習在醫療保健分析項目中的應用》適用於數據科學家、機器學習工程師和醫療保健專業人士,他們希望實施機器學習算法來構建智能人工智能應用程序。預計讀者具備Python或任何編程語言的基本知識,以從本書中獲得最大收益。

目錄:
1. 乳腺癌檢測
2. 糖尿病發作檢測
3. DNA分類
4. 使用機器學習診斷冠狀動脈疾病
5. 使用機器學習篩查自閉症譜系障礙的兒童