Pattern and Data Analysis in Healthcare Settings

by Vivek Tiwari, Basant Tiwari, Ramjeevan Singh Thakur, Shailendra Gupta

Overview

Business and medical professionals rely on large data sets to identify trends or other knowledge that can be gleaned from the collection of it. New technologies concentrate on data's management, but do not facilitate users' extraction of meaningful outcomes. Pattern and Data Analysis in Healthcare Settings investigates the approaches to shift computing from analysis on-demand to knowledge on-demand. By providing innovative tactics to apply data and pattern analysis, these practices are optimized into pragmatic sources of knowledge for healthcare professionals. This publication is an exhaustive source for policy makers, developers, business professionals, healthcare providers, and graduate students concerned with data retrieval and analysis.

Product Details

ISBN-13:	9781522505365
Publisher:	IGI-Global
Publication date:	08/31/2016
Pages:	382
Product dimensions:	8.50(w) x 11.00(h) x 0.88(d)

Editorial Reviews

Reviewer: Pooja Sethi, MD (East Tennessee State University Quillen College of Medicine)
Description: Business and medical professionals rely on large data sets to identify trends and other knowledge that can be gleaned from it. This book investigates the approaches to shift computing from "analysis on demand" to "knowledge on demand." By providing innovative tactics to apply data and pattern analysis, this book provides an exhaustive resource for data retrieval and analysis.
Purpose: Dedicated to data analysis, patterns, and their utility, particularly in healthcare settings, this book aims to investigate all approaches that help with efficient and flexible data analysis. It attempts to uncover and discuss strategies that improve an organization's pattern management strategies for quick and efficient decision-making processes in business technology and healthcare domains.
Audience: The book is aimed primarily at students, researchers, policy makers, and academicians who are interested in developing pattern management system for business application in healthcare decision making.
Features: This is a collaborative effort of researchers and practitioners from India who are well published in the field of data mining and data analysis. The book is organized into three well laid out sections. The first deals with healthcare settings and security in five chapters. The first one discusses techniques that help develop a decision making system which aids in possible reclassification of objects from one state to another. It helps break down an extensive number of rules and develop new decision models which aid in better patient diagnosis. Chapter 2 discusses ways to securely store and transmit healthcare records. Chapter 3 covers fast medical image segmentation using energy-based methods, while chapter 4 deals with time variant key or automatic variable key approaches for information security. Chapter 5 discusses an innovative way to improve intrusion detection using genetic algorithms with a layered approach. The second section covers knowledge visualization and big data. The four chapters in this section cover knowledge extraction from domain specific documents, semiautomatic ontology design for educational purposes, improving multimodality image fusion through Integrate AFL and Wavelet transform, and the architecture and workings of NoSQL database and the differences between it and traditional databases. The final section explores data mining in five chapters. Chapter 10 uses the example of the Bombay Stock Exchange of India to discuss predicting patterns and trends using data mining techniques. This chapter discusses many techniques, such as linear regression, multiple regression, association rule mining, clustering, and neural networks. Chapter 11 discusses profit pattern mining using vague set and genetic algorithms, while chapter 12 covers nonlinear classifiers like classification and regression tree, boosted tree and random forest on the effect of music in native vs. non-native language on the autonomic nervous system. Chapter 13 summarizes the different document clustering techniques used by researchers, and chapter 14 discusses cluster analysis with different algorithms for mixed data. Chapters are well laid out and use tables and figures for better understanding. They end with conclusions and future research directions, as well as references.
Assessment: This is a comprehensive resource for healthcare providers, professionals, and policy makers interested in data analysis and its retrieval in healthcare settings.

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