Big Data Analytics in Healthcare

This chapter provides better understanding and use-cases of big data in healthcare. The healthcare industry generates lot of data every day, and without proper analytical tools, it is quite difficult to extract meaningful data. It is essential to understand big data tools since the traditional devices don't maintain this vast data, and big data solves the major issue in handling massive healthcare data. Health data from numerous health records are collected from various sources, and this massive data is put together to form the big data. Conventional database cannot be used in this purpose due to the diversity in data formats, so it is difficult to merge, and so it is quite impossible to process. With the use of big data this problem is solved, and it can process highly variable data from different sources.

Emerging Trends of Big Data in Cloud Computing

In today's world where data is accumulating at an ever-increasing rate, processing of this big data was a necessity rather than a need. This required some tools for processing as well as analysis of the data that could be achieved to obtain some meaningful result or outcome out of it. There are many tools available in market which could be used for processing of big data. But the main focus on this chapter is on Apache Hadoop which could be regarded as an open source software based framework which could be efficiently deployed for processing, storing, analyzing, and to produce meaningful insights from large sets of data. It is always said that if exponential increase of data is processing challenge then Hadoop could be considered as one of the effective solution for processing, managing, analyzing, and storing this big data. Hadoop versions and components are also illustrated in the later section of the paper. This chapter majorly focuses on the technique, methodology, components, and methodologies adopted by Apache Hadoop software framework for big data processing.

Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)

Chronic kidney disease (CKD) is a disorder in which the kidneys are weakened and become unable to filter blood. It lowers the human ability to remain healthy. The field of biosciences has progressed and produced vast volumes of knowledge from electronic health records. Heart disorders, anemia, bone diseases, elevated potassium, and calcium are the very prevalent complications that arise from kidney failure. Early identification of CKD can improve the quality of life greatly. To achieve this, various machine learning techniques have been introduced so far that use the data in electronic health record (EHR) to predict CKD. This chapter studies various machine learning algorithms like support vector machine, random forest, probabilistic neural network, Apriori, ZeroR, OneR, naive Bayes, J48, IBk (k-nearest neighbor), ensemble method, etc. and compares their accuracy. The study aims in finding the best-suited technique from different methods of machine learning for the early detection of CKD by which medical professionals can interpret model predictions easily.

Big Data and Big Data Analytics

Big data is emerging, and the latest developments in technology have spawned enormous amounts of data. The traditional databases lack the capabilities to handle this diverse data and thus has led to the employment of new technologies, methods, and tools. This research discusses big data, the available big data analytical tools, the need to use big data analytics with its benefits and challenges. Through a research drawing on survey questionnaires, observation of the business processes, interviews and secondary research methods, the organizations, and companies in a small island state are identified to survey which of them use analytical tools to handle big data and the benefits it proposes to these businesses. Organizations and companies that do not use these tools were also surveyed and reasons were outlined as to why these organizations hesitate to utilize such tools.

Applications of Big Data in Large and Small Systems

Nowadays, big data is available in every field due to the advent of computers and electronic devices and the advancement of technology. However, analysis of this data requires new technology as the earlier designed traditional tools and techniques are not sufficient. There is an urgent need for innovative methods and technologies to resolve issues and challenges. Soft computing approaches have proved successful in handling voluminous data and generating solutions for them. This chapter focuses on basic concepts of big data along with the fundamental of various soft computing approaches that give a basic understanding of three major soft computing paradigms to students. It further gives a combination of these approaches namely hybrid soft computing approaches. Moreover, it also poses different applications dealing with big data where soft computing approaches are being successfully used. Further, it comes out with research challenges faced by the community of researchers.

A Review on Critical Success Factors for Big Data Projects

For an organization every year, a large amount of information is generated regarding its employees, customers, business partners, suppliers, etc. Volume, which is one of the attributes of big data, is aptly named because of the vast number of data sources and the size of data generated by these sources. Big data solutions should not only focus on the technological aspects, but also on the challenges that may occur during the project lifecycle. The main purpose of this research is to build on the current diverse literature around big data by contributing discussion on factors that influence successful big data projects. The systematic literature review adopted in this study includes relevant research regarding such critical success factors that are validated in previous studies. The study compiled these critical success factors as provided in the literature regarding big data projects. Notable success factors for big data projects were compiled from literature such as case studies, theoretical observations, or experiments.

Big Data in Cloud Computing

The huge size of data that has been produced by applications that spans from social network to scientific computing is termed big data. Cloud computing as a delivery model for IT services enhances business productivity by reducing cost. It has the intention of achieving solution for managing big data such as high dimensional data sets. Thus, this chapter discusses the background of big data and cloud computing. It also discusses the various application of big data in detail. The various related work, research challenges of big data in cloud computing, and the future direction are addressed in this chapter.

Heart Disease Prediction Using Decision Tree and Random Forest Classification Techniques

Data mining is the approach which can extract useful information from the data. The prediction analysis is the approach which can predict future possibilities based on the current information. The authors propose a hybrid classifier to carry out the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps, which are data pre-processing, feature extraction, and classification. In this research, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. The authors show the results of proposed model using the Python platform. Moreover, the results are compared with support vector machine (SVM) and k-nearest neighbour classifier (KNN).

Technologies and Applications of Internet of Things (IoT) in Healthcare

Developments in technology have opened new doors for healthcare to improve the treatment methods and prevent illnesses as a proactive method. Internet of things (IoT) technologies have also improved the self-management of care and provided more useful data and decisions to doctors with data analytics. Unnecessary visits, utilizing better quality resources, and improving allocation and planning are main advantages of IoT in healthcare. Moreover, governments and private institutions have become a part of this new state-of-the-art development for decreasing costs and getting more benefits over the management of services. In this chapter, IoT technologies and applications are explained with some examples. Furthermore, deep learning and artificial intelligence (AI) usage in healthcare and their benefits are stated that artificial neural networks (ANN) can monitor, learn, and predict, and the overall health severity for preventing serious health loss can be estimated and prevented.

Predictive Strength of Ensemble Machine Learning Algorithms for the Diagnosis of Large Scale Medical Datasets

Machine learning is not a simple technology but an amazing field having more and more to explore. It has a number of real-time applications such as weather forecast, price prediction, gaming, medicine, fraud detection, etc. Machine learning has an increased usage in today's technological world as data is growing in volumes and machine learning is capable of producing mathematical and statistical models that can analyze complex data and generate accurate results. To analyze the scalable performance of the learning algorithms, this chapter utilizes various medical datasets from the UCI Machine Learning repository ranges from smaller to large datasets. The performance of learning algorithms such as naïve Bayes, decision tree, k-nearest neighbor, and stacking ensemble learning method are compared in different evaluation models using metrics such as accuracy, sensitivity, specificity, precision, and f-measure.