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The 13 chapters of this book present scientific concepts, frameworks and ideas on biomedical data analytics and information retrieval 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 image processing, and biomedical natural language processing.

In Chapter 1, “Mortality Prediction of ICU Patients Using Machine Learning Techniques,” Babita Majhi, Aarti Kashyap and Ritanjali Majhi present a mortality prediction using machine learning techniques. Since the intensive care unit (ICU) admits very ill patients, facilitating their care requires serious attention and treatment using ventilators and other sophisticated medical equipment. This equipment is very costly; hence, its optimized use is necessary. ICUs require a higher number of staff in comparison to the number of patients admitted for regular monitoring. In brief, ICUs involve a larger budget compared to other sections of any hospital. Therefore, to help doctors determine which patient is more at risk, mortality prediction is an important area of research. In data mining, mortality prediction is a binary classification problem, i.e., die or survive. As a result, this has attracted machine learning groups to apply algorithms to do the mortality prediction. In this chapter, six different machine learning methods, functional link artificial neural network (FLANN), support vector machine (SVM), discriminate analysis (DA), decision tree (DT), naïve Bayesian network and K-nearest neighbors (KNN), are used to develop a model for mortality prediction collecting data from PhysioNetChallenge 2012 and did the performance analysis of them.

In Chapter 2, “Artificial Intelligence in Bioinformatics,” V. Samuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta and Arpana Vibhuti emphasize the various smart tools available in the field of biomedical and health informatics. They also analyzed recently introduced state-of-the-art bioinformatics using complex AI algorithms.

In Chapter 3, “Predictive Analysis in Healthcare Using Feature Selection,” Aneri Acharya, Jitali Patel and Jigna Patel describe various methods to enhance the performance of machine learning models used in predictive analysis. The chronic diseases of diabetes and hepatitis are explored in this chapter with an experiment carried out in four tasks.

In Chapter 4, “Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications,” Deepanshu Bajaj, Bharat Bhushan and Divya Yadav present the idea of Industry 4.0, which is massively evolving as it is essential for the medical sector, including the internet of things (IoT), big data (BD) and blockchain (BC), the combination of which are modernizing the overall framework of e-health. They analyze the implementation of the I4.0 (Industry 4.0) technology in the medical sector, which has revolutionized the best available approaches and improved the entire framework.

In Chapter 5, “Improved Social Media Data Mining for Analyzing Medical Trends,” Minakshi Sharma and Sunil Sharma discuss social media health records. Nowadays, social media has become a prominent method of sharing and viewing news among the general population. It has become an inseparable part of our lives, with people spending most of their time on social media instead of on other activities. People on media, such as Twitter, Facebook or blogs, share their health records, medication history and personal views. For social media resources to be useful, noise must be filtered out and only the important content must be captured excluding the irrelevant data, depending on the similarities to the social media. However, even after filtering the content, it may contain irrelevant information, so the information should be prioritized based on its estimated importance. Importance can be estimated with the help of three factors: media focus (MF), user attention (UA) and user interaction (UI). In the first factor, media focus is the temporal popularity of that topic in the news. In the second factor, the temporal popularity of a topic on twitter indicates its user attention. In the third factor, the interaction between the social media users on a topic is referred to as the user interaction. It indicates the strength of a topic in social media. Hence, these three factors form the basis of ranking news topics and thus improve the quality and variety of ranked news.

In Chapter 6, “Bioinformatics: An Important Tool in Oncology” Gaganpreet Kaur, Saurabh Gupta, Gagandeep Kaur, Manju Verma and Pawandeep Kaur provide an analysis of comprehensive details of the beginning, development and future perspectives of bioinformatics in the field of oncology.

In Chapter 7, “Biomedical Big Data Analytics Using IoT in Health Informatics,” Pawan Singh Gangwar and Yasha Hasija present are view of healthcare big data analytics and biomedical IoT and aim to describe it. Wearable devices play a major role in various environmental conditions like daily continuous health monitoring of people, weather forecasting and traffic management on roads. Such mobile apps and devices are presently used progressively and are interconnected with telehealth and telemedicine through the healthcare IoT. Enormous quantities of data are consistently generated by such kinds of devices and are stored on the cloud platforms. Such large amounts of biomedical data are periodically gathered by intelligent sensors and transmitted for remote medical diagnostics.

In Chapter 8, “Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline,” Anusuya Pal, Amalesh Gope and Germano S. Iannacchione have an important discussion about how statistical image data are monitored and analyzed. It is revealed that the image processing techniques can be used to understand and quantify the textural features that emerge during the drying process. The image processing methodology adopted in this chapter is certainly useful in quantifying the textural changes of the patterns at different saline concentrations those dictate the ubiquitous stages of the drying process.

In Chapter 9, “Introduction to Deep Learning in Health Informatics,” Monika Jyotiyana and Nishtha Kesswani discuss deep learning applications in biomedical data. Because of the vital role played by biomedical data, this is an emergent field in the health sector. These days, health industries focus on the correct and on-time treatment provided to the subject for their betterment while avoiding any negative aspects. The huge amount of data brings enormous opportunities as well as challenges. Deep learning and AI techniques provide a sustainable environment and enhancement over machine learning and other state-of-the-art theories.

In Chapter 10, “Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review,” Shikha Gupta, Nitish Mehndiratta, Swarnim Sinha, Sangana Chaturvedi and Mehak Singla review the latest literature belonging to the intercessions for data mining in mental health covering many techniques and algorithms linked with data mining in the most prevalent diseases such as Alzheimer’s, dementia, depression, schizophrenia and bipolar disorder. Some of the academic databases used for this literature review are Google Scholar, IEEE Xplore and Research Gate, which include a handful of e-journals for study and research-based materials.

In Chapter 11, “Deep Learning Applications in Medical Image Analysis,” Ananya Singha, Rini Smita Thakur and Tushar Patel present detailed information about deep learning and its recent advancements in aiding medical image analysis. Also discussed are the variations that have evolved across different techniques of deep learning according to challenges in specific fields; and emphasizes one such extensively used tool, convolution neural network (CNN), in medical image analysis.

In Chapter 12, “Role of Medical Image Analysis in Oncology,” Gaganpreet Kaur, Hardik Garg, Kumari Heena, Lakhvir Singh, Navroz Kaur, Shubham Kumar and Shadab Alam give deep insight into the cancer studies used traditionally and the use of modern practices in medical image analysis used for them. Cancer is a disease caused due to uncontrolled division of cells other than normal body cells in any part of the body. It is among one of the most dreadful diseases affecting the whole world; moreover, the number of people suffering from this fatal disease is increasing day by day.

In Chapter 13, “A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection,” Chandra Sekhar Biswal, Subhendu Kumar Pani and Sujata Dash analyze the performance of classifiers using particle swarm optimization-based feature selection. Medical science researchers can collect several patients’ data and build an effective model by feature selection methods for better prediction of disease cure rate. In other words, the data acts just as an input into some kind of competitive decision-making mechanism that might place the company ahead of its rivals.