Preface

      Introduction

The novel applications of Big Data Analytics and machine intelligence in biomedical and healthcare sector can be regarded as an emerging field in computer science, medicine, biology application, natural environmental engineering, and pattern recognition. The use of various Data Analytics and intelligence techniques are nowadays successfully implemented in many healthcare sectors. Biomedical and Health Informatics is a new era that brings tremendous opportunities and challenges due to easily available plenty of biomedical data. Machine learning presenting tremendous improvement in accuracy, robustness, and cross-language generalizability over conventional approaches. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant biomedical, and healthcare data. Earlier, it was common requirements to have a domain expert to develop a model for biomedical or healthcare; but now the patterns are learned automatically for prediction. Due to the rapid advances in intelligent algorithms have established the growing significance in healthcare data analytics. The IoT focuses to the common idea of things that is recognizable, readable, locatable, controllable, and addressable via the Internet. Intelligent Learning aims to provide computational methods for accumulating, updating and changing knowledge in the intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In Future Big data analytics has the impending capability to change the way we work and live. With the influence and the development of the Big Data, IoT concept, the need for AI (Artificial Intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent system generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Intelligent systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics etc.

      This book covers the latest advances and developments in health informatics, data mining, machine learning and artificial intelligence, fields which to a great extent will play a vital role in improving human life. All the researchers and practitioners will be highly benefited those are working in field of biomedical, health informatics, Big Data Analytics, IoT and Machine Learning. This book would be a good collection of state-of-the-art approaches for Big Data and Intelligent based biomedical and health related applications. It will be very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book would be very useful because there is no book in the market which provides a good collection of state-of-the-art methods of Big Data, machine learning and IoT in Biomedical and Health Informatics. Various models for biomedical and health informatics is recently emerged and very unmatured field of research in biomedical and healthcare. This book would be very useful because there is no book in the market which provides a good collection of state-of-the-art methods of for Big data analytics based models for healthcare.

       Organization of the Book

      The 12 chapters of this book present scientific concepts, frameworks and ideas on biomedical data analytics from the different biomedical domains. The Editorial Advisory Board and expert reviewers have ensured the high caliber of the chapters through careful refereeing of the submitted papers. For the purpose of coherence, we have organized the chapters with respect to similarity of topics addressed, ranging from issues pertaining to the internet of things for biomedical engineering and health informatics, computational intelligence for medical data processing and Internet of medical things.

In Chapter 1, “An Introduction to Big Data Analytics Techniques in Healthcare”. Anil Audumbar Pise presents the use of big data analytics in medicine and healthcare which is incredibly powerful, productive, interesting, and diverse. It integrates heterogeneous data like medical records, experimental, electronic health, and social data in order to explore the relations among the different characteristics and traces of data points like diagnoses and medication dosages, along with information such as public chatter to derive conclusions about outcomes. More diverse data needs to be combined into big data analysis, such as bio-sciences, sensor informatics, medical informatics, bioinformatics, and health computational biomedicine to get the truth out of its information.

      In Chapter 2, “Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia” Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam, Mohammed Siddique developed predictive models using four supervised machine learning techniques namely C5.0 Decision tree, Random Forest, Support Vector Machine and Naïve Bayes algorithms using the 2016 EDHS dataset of 10,641 records. The Ethiopian government doing for the past two decades for attaining millennium development goals agenda for preventing childhood mortality by improving the child health’s to change the country image to the rest of the world in reduction of childhood mortality. This study contributes some values in the improvement of childhood health by analyzing the determinants infant and child mortality by using machine learning techniques. Different reports indicate that the distribution of childhood mortality differs in the world.

      In Chapter 3, “Pre-Trained CNN Models in Early Alzheimer’s Prediction Using Post-Processed MRI” Kalyani Gunda and Pradeepini Gera test MRI Scan with Dementia or Not by Non-image MRI Evidence using Random Forest Classifier which obtained 87% accuracy without false prediction and also by predicting Alzheimer’s Progression using advanced CNN models. Gentle Dementia is more focused to train the Early Detection by omitting converted MRI Sessions. Various Transfer Learning Deep Neural Networks like Residual Network (ResNet50), GoogleNet, VGG19 (Visual Geometric Group), MobileNet, AlexNet is compared to classify Alzheimer’s. Model comparison evaluated to explicate model efficacy.

In Chapter 4, “Robust Segmentation Algorithms for Retinal