Physical Characteristics of Type 1 and 2 Diabetic Subjects: NCR, Indian-Based Computational Perspective

Physical Characteristics of Type 1 and 2 Diabetic Subjects: NCR, Indian-Based Computational Perspective

Rohit Rastogi (Dayalbagh Educational Institute, Agra, India & ABES Engineering College, Ghaziabad, India), Devendra K. Chaturvedi (Dayalbagh Educational Institute, Agra, India), Parul Singhal (ABES Engineerig College, Ghaziabad, India) and Mayank Gupta (Tata Consultancy Services, Noida, India)
Copyright: © 2021 |Pages: 36
DOI: 10.4018/978-1-7998-2791-7.ch010
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Abstract

The Delhi and NCR healthcare systems are rapidly registering electronic health records, diagnostic information available electronically. Furthermore, clinical analysis is rapidly advancing—large quantities of information are examined and new insights are part of the analysis of this technology—and experienced as big data. It provides tools for storing, managing, studying, and assimilating large amounts of robust, structured, and unstructured data generated by existing medical organizations. Recently, data analysis data have been used to help provide care and diagnose disease. In the current era, systems need connected devices, people, time, places, and networks that are fully integrated on the internet (IoT). The internet has become new in developing health monitoring systems. Diabetes is defined as a group of metabolic disorders affecting human health worldwide. Extensive research (diagnosis, path physiology, treatment, etc.) produces a great deal of data on all aspects of diabetes. The main purpose of this chapter is to provide a detailed analysis of healthcare using large amounts of data and analysis. From the Hospitals of Delhi and NCR, a sample of 30 subjects has been collected in random fashion, who have been suffering from diabetes from their health insurance providers without disclosing any personal information (PI) or sensitive personal information (SPI) by law. The present study aimed to analyse diabetes with the latest IoT and big data analysis techniques and its correlation with stress (TTH) on human health. Authors have tried to include age, gender, and insulin factor and its correlation with diabetes. Overall, in conclusion, TTH cases increase with age in case of males and do not follow the pattern of diabetes variation with age while in the case of female TTH pattern variation (i.e., increasing trend up to age of 60 then decreasing).
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Introduction

Role of Big Data in Healthcare

Using Big Data analysis in the healthcare can be very positive and save lives. Large-scale data refers to massive data generated by digitizing all items referenced by integrating and analyzing a particular technology. For health care, it uses specific health information from the population (or specific people) to help prevent potential pandemic diseases, treat illness, reduce costs etc.(McAfee, 2012).

As we have lived for a long time, treatment models have changed, and many of these changes are based on data.

Physicians want to fully understand about the best possible about the patient and alerting the signs of serious illness early in life; treating the illness early is much easier and cheaper.

By analyzing health data, prevention is better than treatment, and managing a patient's perspective allows insurance to deliver the right package. This is an industry initiative to address silo issues with patient information. The bits and bytes are collected everywhere, archived in hospitals, clinics, surgery etc, there is no possibility of unreliable communication (McAfee, 2012).

Big Data Characteristics and It’s Benefits in Healthcare

The concepts of Big Data are not new, but its definitions are constantly changing. In various efforts to define large-scale data, basically, A set of data elements complexity require the search, whose size, type, adoption and invention of new hardware and software mechanisms call analyze and visualize information.(Rastogi, 2018)

Health is a simple example that shows that three V data, speed (data generation speed), size, and volume are the essential aspects of the data generated. These data are distributed to various medical systems, insurance companies, researchers, researchers and government agencies. Furthermore, each of these data tanks is inherently unstable and cannot provide (Jacobs, 2009).

A platform for global data transparency. In summation to the three V's, healthcare data are too essential for meaningful usage of it to develop translational research.

The potential data and Benet data in the development and implementation of large-scale data solutions in this area, despite the inherent complexity of healthcare (Jacobs, 2009).

Benefits: Create 360 ​​degree views of consumers, patients, and doctors.

  • Improve personalization and care with a comprehensive patient profile.

  • Follow your doctor's preferences, referrals, and clinical reservation data to let the doctor know how to manage your reservation.

  • Improve healthcare marketing efforts with information about consumers, patients and physicians' needs and preferences.

  • Analyze trends in hospitals and larger healthcare networks to help research and care improve people's health.

  • Identify health outcomes, patient satisfaction, and patient tissue patterns.

  • Predict health outcomes by using data analysis and developing preventative care strategies.

  • Optimization of growth by improving efficiency, efficiency and care compliance.

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The Future Of Healthcare Big Data

In the future, the healthcare providers will be adding significant amounts of data as they are critical to success. Healthy data continues to support smarter, more integrated touch marketing.

In addition, with the growth of wearable technology and Internet objects (IoT), a large amount of data will be available. Permanent monitoring of patients with wearable technology and IoT is the norm, adding more information to large data stores (As per Fig.1).

Figure 1.

Sources of big data in healthcare (MILLER, 2018)

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