Healthcare Data Analysis Using Deep Learning Paradigm
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K. Renuka Devi
Abstract
In the present decades, analysis of healthcare domain plays a significant role for research purposes and it depends more on computer technology. The analysis of medical data is one of the most primary factors for healthcare domain that has to be done by the machine learning (ML) intelligent systems. Normally, the healthcare data is composed of huge volume of sensitive data such as patient’s information, health insurance information, and patient’s medical histories. The handling and analyzing those data for extracting important facts is one of the tedious process. To resolve this issue, ML plays a vital role in analyzing the above gathered data. ML utilizes several statistical methods as well as complex algorithms to process the healthcare data to predict the results. For efficient data analysis, those methods are categorized as supervised, unsupervised, and semi-supervised. To analyze those sensitive data in a deeper manner, the neural network under ML approach promotes a significant role. In-specific, deep learning (DL) models are utilized for pattern recognition in the healthcare domain. DL is a component of the artificial intelligence and machine learning (AI/ML) paradigm that mimics how people learn. So, this technique is highly recommended for analyzing and recognizing patterns in clinical data. This chapter aims to provide detailed review of various DL approaches, DL algorithms, different DL applications, and its utilization in healthcare, followed by its issues and challenges.
Abstract
In the present decades, analysis of healthcare domain plays a significant role for research purposes and it depends more on computer technology. The analysis of medical data is one of the most primary factors for healthcare domain that has to be done by the machine learning (ML) intelligent systems. Normally, the healthcare data is composed of huge volume of sensitive data such as patient’s information, health insurance information, and patient’s medical histories. The handling and analyzing those data for extracting important facts is one of the tedious process. To resolve this issue, ML plays a vital role in analyzing the above gathered data. ML utilizes several statistical methods as well as complex algorithms to process the healthcare data to predict the results. For efficient data analysis, those methods are categorized as supervised, unsupervised, and semi-supervised. To analyze those sensitive data in a deeper manner, the neural network under ML approach promotes a significant role. In-specific, deep learning (DL) models are utilized for pattern recognition in the healthcare domain. DL is a component of the artificial intelligence and machine learning (AI/ML) paradigm that mimics how people learn. So, this technique is highly recommended for analyzing and recognizing patterns in clinical data. This chapter aims to provide detailed review of various DL approaches, DL algorithms, different DL applications, and its utilization in healthcare, followed by its issues and challenges.
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
