A Comprehensive study of Big Data Analytics and Tools for ...

Journal on Recent Innovation in Cloud Computing, Virtualization & Web Applications Vol. 4, Issue 1 – 2020

ISSN: 2581-544X

© Eureka Journals 2020. All Rights Reserved. 3

A Comprehensive study of Big Data Analytics and Tools for Health care analysis

Shweta Sharma1 1Aryabhatta College of Engg. & Research Centre, Ajmer, India.

Abstract

It is the concept of Big data and a variety of domains. The purpose is to encapsulate the structures of big data, examination methods, applications, and experiments in healthcare. Healthcare in big data has its structures, such as heterogeneity, privacy, and ownership, longevity. These structures convey a series of experiments for mining and promote involvement in health-related research. To analysis focusing the approaches on big data and deal with these challenges in healthcare, regulations, and laws need to create for big data in healthcare and identifies and enacted all the possible challenges benefits the realizing of health care. Different healthcare as well as in big data including predictive analytics, prescriptive analytics, and data analytics. A big data presentation of big data study and patient perspective enhanced dealing, and subordinate budgets. In accumulation to government, research, patients, also hospitals advantage from the institutions of big data in health care.

Keywords: Big Data Analytics, Commercial platform for healthcare, Data Preparation, Implementation Process.

Introduction

The extent of old individuals in the public eye is developing around the world; this wonder known as mankind's maturing has numerous ramifications on medical care administrations, particularly regarding costs [1]. Even with such a circumstance, depending on traditional frameworks may bring about a daily existence quality decay for a huge number of individuals. Trying to beat this issue, a lot of medical care frameworks have been planned. Their basic guideline is moving, on a periodical premise, clinical boundaries like circulatory strain, pulse, glucose level, internal heat level, and ECG signs to a robotized framework pointed toward checking progressively patients' medical issue. Such frameworks give fast help when required since information is examined persistently. Mechanizing health checking that proactive methodology favors a pacifies clinical offices through saving costs recognized by hospitalization, and it additionally upgrades medical care administrations through refining sitting tight an ideal opportunity for conferences [2].


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© Eureka Journals 2020. All Rights Reserved. Page 14

Descriptive Analytics: This includes citing current conditions and writing on them. Some methods have been to perform the employed of this level of analytics. Used, for example, descriptive statistics implements such as graphs and histograms are among the techniques used in descriptive analysis [3].

Diagnostic Analysis: It intends to clarify why definite special occasions happened and what are the components that set them up. For instance, analytical investigations attempt to explain some patients' explanations of general reading using certain techniques, for example, group and tree of choice [4].

Predictive Analytics: It shows the ability to predict future opportunities; This additionally helps in identifying patterns and deciding the likelihood of suspicious results. If a patient can get entangled, then his job is to be delimited. The presented model is often used using AI methods [5].

Prescriptive Analytics: This will likely propose appropriate activities that motivate an ideal dynamic. For example, predetermined investigations may propose to reject a given treatment because of the high probability of an unsafe outcome. It outlines the investigation steps for the medical care space. The congruence of large information innovations in medical services examination may indicate better execution of the diagnostic framework [6].

Figure 1.Healthcare Analytics

In element, huge information alludes to enormous that datasets consolidate the accompanying qualities which volume alludes to high measures of information, a speed which implies that information is produced at a quick speed, an assortment which underscores that information goes under various configurations, and, at long last, veracity which implies that information starts from trustable sources [7].

The information over-burden

These perceptions have become so prominent that have in the long run prompted the introduction of another field of science named 'Information Science'. Information science manages different perspectives including information on the board and investigation [8], to extricate further experiences for improving the usefulness or administrations of a framework (for instance, medical care and transport framework). Furthermore, with the accessibility of probably the most


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imaginative and significant approaches to picture large information post-investigation, it has gotten more obvious the working of any unpredictable framework. As an enormous part of society is getting mindful of and associated with producing huge information, it has gotten important to characterize what large information is. Consequently, in this survey, we endeavor to give subtleties on the effect of enormous information in the change of the worldwide medical care area and its effect on our day by day lives [9].

Consistently, individuals working with various associations across the planet are preparing a large measure of information. The expression "advanced universe" is characterized by such vast measures of quantitative information, which are repeated and eaten in solitary years. It symbolizes the incredible speed at which the computerized universe is expanding. Web sites such as Google and Facebook are collecting huge measures of Goliath information [10].

Characterizing enormous information

Even though various definitions for huge information exist, the most famous definition was given by Douglas Laney and very much acknowledged [11]. That saw (enormous) information was filling in three distinct measurements, in particular, volume, speed, and assortment. The 'enormous' piece of huge information is demonstrative of its huge volume. Notwithstanding volume, the huge information portrayal additionally incorporates speed and assortment. Speed demonstrates the speed or pace of information assortment and making it available for additional examination; while, assortment comments on the various kinds of coordinated and sloppy information that any firm or framework can gather, for example, exchange level information, video, sound, text, or log documents [12].

The expression "vast information" has recently gained much mainstream worldwide. Every field of examination, whether it identifies with industry or scholars, is producing and dissecting vast information for various purposes [13]. The most difficult undertaking about this tremendous pile of information that can be coordinated and chaotic is its administration. Given the way that enormous information is unmanageable utilizing customary programming, we need progressed applications and programming that can use quick and cost-proficient very good quality computational force for such errands. Execution of man-made brainpower (AI) calculations and novel combination calculations would be important to bode well from this huge measure of information [14]. It would be an extraordinary accomplishment to accomplish mechanized dynamics by the execution of AI (ML) strategies like neural organizations and other AI methods. Nonetheless, without fitting programming and equipment support [15], huge information can be very murky. We need to grow better methods to deal with this 'perpetual ocean' of information and brilliant web applications for proficient examination to acquire functional bits of knowledge. With legitimate capacity and scientific apparatuses close by, the data and experiences got from can make the huge information basic social framework parts and administrations (like medical care, security) extra mindful, intuitive, and effective. What's more, the representation of huge


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information in an easy-to-understand way will be a basic factor for the cultural turn of events [16].

Data Research

Handling raw information deprived of program planning may need additional computational assets that are not moderate in a major information setting [17]. Consequently, information is determined to ensure that it is properly organized, to acquire accurate presenter models, and to upgrade the unbreakable quality of information mining strategies. The information system consists of two stages: information cleaning and information transfer. Handling raw information without program planning may require additional computational assets that are not moderate in a major information setting [18]. As a result, information is set to ensure that accurate presenter models are obtained and the unbreakable quality of information mining strategies is upgraded. The information system consists of two stages: information cleaning and information transfer [19].

Information Filtering: Information sifting within the sight of enormous size information is accomplished by disposing of data that isn't helpful for healthcare checking dependent on a characterized rule [20].

Information Cleaning: It incorporates a few parts like standardization, commotion decrease, and missing information management [21]. A few strategies are used to kill boisterous to discover the information and benefits of disappeared information. Indeed, medical records often incorporate boisterous data and can have missing information. Deciding missing qualities in healthcare information is a basic interaction. In the healthcare area, the treatment of missing information ought to be performed with the most extreme exactness as off-base choices may have genuine results. The information mining field has numerous amazing calculations pointed toward dealing with missing qualities, for example, Expectation-Maximization (EM) calculation and various Imputation calculation [22].

Clamor Treatment

When all is said in done, boisterous information is treated by two primary methodologies. The first comprises of remedying loud qualities dependent on information cleaning methods; these strategies are hard to execute and are applied uniquely on account of modest quantities of clamor. The subsequent methodology depends on clamor channels, which decide and kill uproarious occasions in the preparation information, and those channels don't present changes on received information mining techniques [21].

For example, (EMRs) Electronic Medical Records show well the requirement for cleaning information as it might give loud information containing deficient data. Information sparsity in EMRs discovers its inception in the sporadic assortment of boundaries over the long haul since


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patient boundaries are verified just after patients are available in medical clinics. On account of biomedical symbolism, many preparing methods have been applied to decrease clamor [22].

By and large, the planning of biomedical pictures begins with the distinguishing proof (division) of huge items. Then again, information readiness is difficult when managing crude web-based media information. Notwithstanding its enormous volume and its casual substance, this sort of information has the basic part of containing the client's data. In this manner, information cleaning is a vital factor for accomplishment in interpersonal organization investigation. At the point when the information readiness step closes, the handled information should be put away in the readied information store [23].

Feature Collection and Abstraction

The multiplication of gadgets intended to gather medical information lately has expanded hugely several highlights both the examples checking in the field of healthcare. Along these lines, choosing the main highlights becomes pivotal when confronting such high volume information; see. In this specific circumstance, a few methods have been proposed to deal with this problem, particularly when taking care of thousands of highlights. Then again, include extraction addresses another methodology that comprises of removing a decreased number of characteristics contrasted with the first ones. Applying highlight determination and extraction strategies requires a factual apparatuses store [24].

Prescient Classical Design

The goal of this segment is to fabricate a model equipped for delivering expectations for ground-breaking perceptions dependent on past information. The nature of an assumed prescient model is assessed by its precision. Those models are created dependent on apparatuses accessible in the factual and AI collection given by the proposed engineering. The after effects of group preparing will be put away in the model store [25].

Stream Processing Layer

The stream information examination layer is made out of an information synchronization module, versatile indicator module, and versatile pre-processor module [26].

Information Synchronization: The job of the information module synchronization is to ensure that information is handled in the right request in regards to the time model. Moreover, the information synchronization measure excuses estimations that are conflicting and deals with missing qualities. Recognition of conflicting qualities is performed by characterizing edges on the approaching boundaries [27].

Versatile Learning: In numerous requests, it is accepted that information pre-processing task is achieved through knowledge calculations, or possibly that information has been now pre-


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processed before its appearance. In most cases, such assumptions don't coordinate with the real world. This is especially valid for our planned framework which concentrates on streaming information from stream temp collection. The essential to adjust despite information changes prompted the advancement of versatile frameworks, and a critical factor for the drawn-out achievement of a major information framework is flexibility. Truth be told, pre-processing isn't an assignment acted autonomously; it is fairly a segment having a place with the versatile framework. Also, to remain dependable and keep a specific level of precision, prescient models should adjust when information changes happen. Therefore, the forecast interaction might be considered as a piece of the versatile framework that will be the relationship of two unmistakable parts that are versatile pre-processor and versatile indicator [29-33].

Batch Processing Layer

Batch computing is implemented on extricated information as arranged information stores through various stages [32].

Information Acquisition

When observing consistently a patient's health condition, a few sorts of information are produced. Medical information may incorporate organized information like customary Electronic Healthcare Records (EHRs), semi structured information, for example, logs delivered by some medical gadgets, and unstructured information produced, for instance, by biomedical symbolism [33].

It contains Electronic Healthcare Records a total patient medical history put away in a computerized design; it is shaped by a huge number of medical information depicting the patient's health position like socioeconomics, prescriptions, analysis, research center tests, specialist's notes, radiology archives, clinical data, and instalment notes. In this way, EHR addresses an important wellspring of data with the end goal of healthcare investigation. Besides, EHR permits trading information among healthcare specialists' networks.

Biomedical imaging is viewed as an integral asset in regards to infection discovery and care conveyance. All things considered, preparing this sort of picture is trying as they incorporate boisterous information that should be disposed of to help doctors settle on precise choices [34].

Interpersonal organization Analysis: Performing informal organization examination requires gathering information from web-based media like interpersonal interaction destinations. The following stage comprises extricating knowledge that could influence healthcare prescient investigation, for example, finding irresistible diseases. By and large, informal organization information is set apart by the vulnerability that makes their utilization in planning prescient models hazardous [35].


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Detecting various kinds of Data: Sensors are used in healthcare checking arrangements. Those gadgets are fundamental in observing a patient's health as they amount to a wide scope of medicinal pointers, for example, internal heat level, circulatory strain, respiratory rate, pulse, and cardiovascular status [36]. To guarantee productive health checking, patients' living territories might be brimming with gadgets like reconnaissance cameras, mouthpieces, and pressing factor sensors. Therefore, information volume produced by health checking frameworks will in general increment immensely which requires embracing refined techniques during the handling stage [37].

Cell Phone: These days, the cell phone addresses perhaps the most mainstream innovative gadgets on the planet. Contrasted with their initial beginnings, cell phones changed from a fundamental specialized device to a perplexing gadget offering numerous highlights and administrations. They are right now furnished with a few devices like satellite situating administrations, accelerometers, and cameras. Because of their numerous abilities and wide use, cell phones are ideal up-and-comers in regards to health information assortment permitting the plan of numerous effective healthcare applications like pregnancy observing, kid nourishment, and heart recurrence checking [38].

The goal of the information procurement stage is to peruse the information assembled from healthcare sensors in a few arrangements and afterward, information moves through the semantic module before being standardized [39].

Mobile Health and Mobile Computing (Health)

In the present computerized world, each separate is by all accounts fixated to follow their wellness and health measurements utilizing the in-constructed pedometer of their versatile and wearable gadgets, for example, cell phones, smart watches, wellness dashboards, or tablets [40]. Through inexorably versatile people in practically all parts of life, the healthcare foundation wants to renovate to oblige cell phones. The practice of medication and general health utilizing cell phones, known as health or portable health, invades various levels of health care particularly for persistent illnesses, like diabetes and malignant growth. Healthcare associations are progressively utilizing versatile health and wellbeing administrations for carrying out novel and imaginative approaches to give care and organize health just as wellbeing. Portable stages can improve healthcare by speeding up intuitive correspondence among patients and healthcare suppliers [41].

AYASDI

Ayasdi is a giant vendor that tries to present a machine intelligence phase through a machine-based intelligence phase, with an application structure with optimization. It gives different applications for clinical investigation, for instance, to realize and monitor clinical diversity and to alter clinical consideration costs. It is additionally suitable for considering and organizing how


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emergency clinics are coordinated, discussions among doctors, options located by doctors for treatment, and ideas to be considered for patients.

Figure 2.“Intelligent Application Suite” application of AYASDI in the

case through for numerous examines such as population, and risk management, health clinical variation in the healthcare sector

Likewise, it gives an application to the evaluation and population health of management, an active system that undergoes previous customary hazard investigation procedures [42-45].

Linguamatics

NLP-based is a computation that relies on an interactive text mining computation [46]. It can I2E cutting and break a wide array of facts. In this method is the results obtained are ten times faster than various tools and do not require expert knowledge for data analysis. This method can reveal hereditary connections and from the facts of unstructured data. Traditionally, machine learning requires well-curated input data to produce clean and Jharkhand results. In any case, when NLP is integrated into a clinical record or EHR encourages the insertion of accurate and structured files that are often covered in unstructured input files [47].

Figure 3.The schematic portrayal of utilized in NLP-based Artificial Intelligence system gigantic

data maintenance and investigation in Linguamatics


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IBM Watson

This Tech Goliath is one of IBM's one-of-a-kind ideas that aims to heavily scrutinize data in every professional area [48]. This step uses large-scale machine learning and artificial intelligence-based computation to extract information that is the maximum and from the input is minimal. It extends the routine of IBM Watson integrating a wide array of healthcare domains to deliver meaningful and structured data. To highlight novel drug targets in malignant growth infection models, It has shaped productive IBM Watson and Pfizer collaborations to increase the speed the revelation of novel susceptible oncology combinations. The combination of Watson's intensive learning modules integrated with the advances of Artificial Intelligence permits analysts to interpret complex genomic data sets. used to IBM Watson has been inferring categorical types of disease based on quality appearance outlines providing indications for a variation of draggable targets derived from a variety of heavy data sets [49-52].

Figure 4.Healthcare Data Examination of IBM Watson

Conclusion

There is a fact that several real problems to prepare signals of physical material to achieve, the current state gave the information of capacity and non-standard structure, each of the cycles in the direction of giving a fundamental increase inside medical investigational exploration there are open doors in progress. And practice network. In addition to the specific requirement for additional examination in place of information fighting, coupling, and blending nonstop and discrete diagnostic information designs, there is a similar requirement to create novel sign handling methods specific to physiological signs. Excavations for biomarkers and outlining examples inside the bio signal have been shown to give remarkable figures for understanding and anticipating cases of disease related to the disease. Nevertheless, there are openings to create calculations to separate information, address entry, transformation, extraction, highlight determination, and more. Also, with the reputation and improvement of AI calculations, for


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improving and producing vigorous CDSS for clinical forecasting. The solution, and diagnosis. A combination framework of physical information and high-quantity "- omics" strategies clinical suggestions convey is a formidable test for scientists. Even though the partner has useful effects with changes in quality, nonstop expansion of advanced, inaccessible genomic information and the associated effects of commenting on its properties and mistakes make controversial and utilitarian implications of high-throughput sequencing processes effort and scientific practices. Huh. Hard venture. Recreation of organizations at the genome scale is a poorly presented issue. It has been the powerful presentations produced used for the recreation of metabolic organizations and quality executive organizations. Restricted access to dynamic constants is a bottleneck and subsequently, various copies attempt to beat this constraint. There is an issue of the understanding of this vast scope as a quality guideline, the effects of various organizational structures, and the results of the development of these organizations are yet to be investigated. To address these concerns, a mix of cautious planning of analysis and model improvement to entertain organizations save time and help the assets spent in structure guidelines in genome-scale organizations. The opportunity to appear in stupid tests requires close involvement between experimenters, computational researchers, and practitioners.

References

1. A. Bánhalmi, J. Borbás, M. Friedrich, V. Bilicki, Z. Gingl, and L. Rudas, "Analysis of a pulse rate variability measurement using a smartphone camera," Journal of Healthcare Engineering, vol. 2018, Article ID 4038034, 15 pages, 2018.

2. S. Sasubilli, A. Kumar, V. Dutt, ''Machine Learning Implementation on Medical Domain to Identify Disease Insights using TMS", 2020, Sixth International Conference on Advances in Computing & Communication Engineering Las Vegas USA ICACCE 2020 (22-24 June) ISBN: 978-1-7281-6362-8.

3. A. Vaughn, P. Biocco, Y. Liu, and M. Anwar, “Activity detection and analysis using smartphone sensors,” in Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration (IRI), pp. 6-9, Salt Lake City, UT, USA, July 2018.

4. Agrebi, S., & Larbi, A. (2020). Use of artificial intelligence in infectious diseases. Artificial intelligence in precision health, 415–438. doi:10.1016/b978-0-12-817133-2.00018-5.

5. S. M. Sasubilli, A. Kumar and V. Dutt, "Improving Health Care by Help of Internet of Things and Bigdata Analytics and Cloud Computing," 2020 International Conference on Advances in Computing and Communication Engineering (ICACCE), Las Vegas, NV, USA, 2020, pp. 1-4, doi: 10.1109/ICACCE49060. 2020.9155042.

6. E. Reinertsen and G. D. Clifford, “A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses,” Physiological Measurement, vol. 39, no. 5, 2018.

7. Abhishek Kumar, Tvm Sairam, Vishal Dutt, “Machine Learning Implementation for Smart Health Records: A Digital Carry Card”, Global Journal on Innovation, Opportunities and Challenges in AAI and Machine Learning Vol. 3, Issue 1-2019.


ISSN: 2581-544X


8. Emily A Holmes, Rory C O’Connor, V Hugh Perry, Irene Tracey, Simon Wessely, Louise Arseneault, Clive Ballard, Helen Christensen, Roxane Cohen Silver, Ian Everall, et al. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. The Lancet Psychiatry, 2020.

9. Henrik Vogt, Sara Green, Claus Thorn Ekstrøm, and John Brodersen. How precision medicine and screening with big data could increase overdiagnosis. BMJ, 366:l5270, 2019.

10. R. Raturi and A. Kumar "An Analytical Approach for Health Data Analysis and finding the Correlations of attributes using Decision Tree and W-Logistic Modal Process", 2019, IJIRCCE Vol 7, Issue 6, ISSN(Online): 2320-9801 ISSN (Print) : 23209798.

11. JHU: John Hopkins University, 2020. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU).https://www. coronavirus.jhu.edu/map. html (accessed 09 June 2020).

12. K. Seetharam, N. Kagiyama, and P. P. Sengupta, "Application of mobile health, telemedicine, and artificial intelligence to echocardiography," Echo Research and Practice, vol. 6, no. 2, pp. 41-52, 2019.

13. Swarn Avinash Kumar, Harsh Kumar, Vishal Dutt, Himanshu Swarnkar, “COVID-19 Pandemic analysis using SVM Classifier: Machine Learning in Health Domain”, Global Journal on Application of Data Science and Internet of Things, 2020, Vol 4 No. 1.

14. L. Xavier, R. Thirunavukarasu, and R. Thirunavukarasu, “A distributed tree-based ensemble learning approach for efficient structure prediction of protein,” International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp. 226–234, 2017.

15. S. Chandrasekaran and A. Kumar Implementing Medical Data Processing with Ann with Hybrid Approach of Implementation Journal of Advanced Research in Dynamical and Control Systems-JARDCS issue 10, vol.10, page 45-52, ISSN-1943-023X. 2018/ 09/15.

16. Li H.-C., Yang G., Yang W., Du Q., Emery W.J. Deep nonsmooth nonnegative matrix factorization network with semi-supervised learning for SAR image change detection. ISPRS J Photogramm Remote Sens. 2020; 160: 167-179. DOI: 10.1016/j.isprsjprs.2019. 12.002.

17. Swarn Avinash Kumar, Harsh Kumar, Vishal Dutt, Pooja Dixit, “Deep Analysis of COVID-19 Pandemic using Machine Learning Techniques”, (2020): Global Journal on Innovation, Opportunities and Challenges in AAI and Machine Learning, Vol 4 No 2, [ISSN: 2581-5156].

18. S. J. Al'Aref, K. Anchouche, G. Singh, et al., "Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging," European Heart Journal, vol. 40, no. 24, pp. 1975-1986, 2019.

19. Vishal Dutt, Sriramakrishnan Chandrasekaran, Vicente García-Díaz, (2020). “Quantum neural networks for disease treatment identification.”, European Journal of Molecular & Clinical Medicine, 7(11), 57-67.

20. S. Majumder and M. J. Deen, “Smartphone sensors for health monitoring and diagnosis,” Sensors, vol. 19, no. 9, p. 2164, 2019.

https://www.


ISSN: 2581-544X


21. Sohrabi C., Alsafi Z., O'Neill N., Khan M., Kerwan A., Al-Jabir A., Losifidis C., Agha R. World health organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19) Int J Surg. 2020 DOI: 10.1016/j.ijsu.2020.02.034.

22. Vikas Kumar Singh, Dr. Sanjay Pawar, Lohit Shekam, Vishal Dutt (2020),” Impact Of Covid 19 On Fmcg Sector.” Journal of Critical Reviews, 7 (12), 4477-4484. doi:10.31838/ jcr.07.12.640.

23. Swarn Avinash Kumar, Harsh Kumar, Vishal Dutt, Pooja Dixit, “The Role of Machine Learning in COVID-19 in Medical Domain: A Survey”, Journal on Recent Innovation in Cloud Computing, Virtualization & Web Applications, Vol 4 No 1 (2020), [ISSN: 2581-544X]

24. T. Zhang, “A joint deep learning and internet of medical things driven framework for elderly patients,” IEEE Access, vol. 8, pp. 75822-75832, 2020.

25. Yipeng Zhang, Hanjia Lyu, Yubao Liu, Xiyang Zhang, Yu Wang, and Jiebo Luo. Monitoring depression trend on Twitter during the covid-19 pandemic. arXiv preprint arXiv:2007.00228, 2020.

26. S. A. Kumar, H. Kumar, V. Dutt and H. Soni, "Self-Health Analysis with Two Step Histogram based Procedure using Machine Learning," 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2021, pp. 794-799, doi: 10.1109/ ICICV50876.2021.9388427.

27. Z. Chen, C. Jiang, and L. Xie, “A novel ensemble ELM for human activity recognition using smartphone sensors,” IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 2691-2699, 2019.

28. Z. F. Khan, "Automated segmentation of lung parenchyma using color-based fuzzy C-means clustering," Journal of Electrical Engineering & Technology, vol. 14, no. 5, pp. 2163–2169, 2019.

29. Swarn Avinash Kumar, Harsh Kumar, Vishal Dutt, Himanshu Swarnkar, “Contribution Of Machine Learning Techniques to Detect Disease In-Patients: A Comprehensive Analysis of Classification Techniques”, Global Journal on Innovation, Opportunities and Challenges in AAI and Machine Learning, Vol. 3, Issue 1 -2019, ISSN: 2581-5156.

30. Global Strategy on human resources for health: Workforce 2030, World Health Organization, 2016, https://www.who.int/hrh/ resources/pub_globstrathrh-2030/en/.

31. Artificial intelligence: Potential benefits and ethical considerations, European Parliament Legal Affairs briefing, Policy Department C: Citizens’ Rights and Constitutional Affairs, PE 571.380, 2016.

32. Pramod Singh Rathore, Vishal Dutt, Pooja Dixit, “Enlightenment Capacity For Powerful Face Recognition Mechanism Using DCT Algorithm”, International Journal of Innovative Research in Computer and Communication Engineering, February 2019, Issue- 2, Volume-7, ISSN(Online): 2320-9801, ISSN (Print) : 2320-9798.

33. “A future that works: Automation, employment and productivity”, McKinsey Global Institute, January 2017; “Artificial intelligence: The next frontier”, McKinsey Global Institute, June 2017.

https://www.who.int/hrh/


ISSN: 2581-544X


34. S. R. Swarna, S. Boyapati, V. Dutt and K. Bajaj, "Deep Learning in Dynamic Modeling of Medical Imaging: A Review Study," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 745-749, doi: 10.11 09/ICISS49785.2020.9315990.

35. Ross, C. and Swetlitz, I. (2017), ‘IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close’, STAT, 5 September 2017, https://www.statnews.com/ 2017/09/05/ watson-ibm-cancer (accessed 11 Mar. 2020).

36. Swarn Avinash Kumar, Harsh Kumar, Vishal Dutt, Himanshu Swarnkar, “Role of Machine Learning in Pattern Evaluation of COVID-19 Pandemic: A Study for Attribute Explorations and Correlations Discovery among Variables”, (2020): Global Journal on Application of Data Science and Internet of Things, Vol 4 No 2, [ISSN: 2581-4370].

37. Safi, S., Thiessen, T. and Schmailzl, K. J. G. (2018), ‘Acceptance and Resistance of New Digital Technologies in Medicine: Qualitative Study’, JMIR research protocols, 7(12), doi: 10.2196/ 11072 (accessed 11 Mar. 2020).

38. S. Boyapati, S. R. Swarna, V. Dutt and N. Vyas, "Big Data Approach for Medical Data Classification: A Review Study," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 762-766, doi: 10.1109/ ICISS49785.2020.9315870.

39. Schmelzer, R (2019), ‘Understanding Explainable AI’, Forbes, 23 July 2019, https://www.forbes.com/sites/cognitiveworld/2019/07/23/understanding-explainable-ai/# 4293beac7c9e (accessed 5 Mar. 2020).

40. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S. and Vertesi, J. (2019), ‘Fairness and Abstraction in Socio technical Systems’, Proceedings of the Conference on Fairness, Accountability, and Transparency, January 2019, pp: 59-68, doi: 10.1145/32 87560.3287598 (accessed 10 Mar. 2020).

41. Selvaraj, S., Farooqui, H. H. and Karan, A. (2018), ‘Quantifying the financial burden of households’ out-of-pocket payments on medicines in India: a repeated cross-sectional analysis of National Sample Survey data, 1994-2014’, BMJ Open, 8(5), doi:10. 1136/bmjopen-2017-018020 (accessed 10 Mar. 2020).

42. Swarn Avinash Kumar, Harsh Kumar, Srinivasa Rao Swarna, Vishal Dutt, “Early Diagnosis and Prediction of Recurrent Cancer Occurrence in a Patient Using Machine Learning”, European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 6785-6794.

43. Shou, D. (2019), ‘The Next Big Privacy Hurdle? Teaching AI to Forget’, WIRED, 6 December 2019, https://www.wired.com/story/ the-next-big-privacy-hurdle-teaching-ai-to-forget (accessed 10 Mar. 2020).

44. Siddiqui, F. R. (2008), ‘Denied treatment, Dalit woman dies’, The Times of India, 25 April 2008, https://timesofindia.indiatimes. com/city/lucknow/Denied-treatment-Dalit-woman-dies/articleshow/ 2980771.cms (accessed 11 Mar. 2020).

https://www.statnews.com/

https://www.forbes.com/sites/cognitiveworld/2019/07/23/understanding-explainable-ai/#

https://www.wired.com/story/

https://timesofindia.indiatimes.


ISSN: 2581-544X


45. Singh, S. (2020), ‘Google, Microsoft circle as India mulls extracting value from health data of 1.3 billion citizens’, The Ken, 6 January 2020, https://the-ken.com/story/google-microsoftindia-pdp-health-data-sharing (accessed 10 Mar. 2020).

46. S. A. Kumar, H. Kumar, V. Dutt and H. Soni, "Self-Health Analysis with Two Step Histogram based Procedure using Machine Learning," 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2021, pp. 794-799, doi: 10.1109/ ICICV50876.2021.9388427.

47. Sinha, S. (2018), ‘How Microsoft Is Expanding Its Healthcare Initiative In India Using AI’, Analytics India Magazine, 20 March 2018, https://analyticsindiamag.com/how-microsoft-is-expandingits-healthcare-initiative-in-india-using-ai (accessed 10 Mar. 2020).

48. Vishal Dutt, Rohit Raturi, Vicente García-Díaz, Sreenivas Sasubilli, “Two-Way Bernoulli distribution for Predicting Dementia with Machine Learning and Deep Learning Methodologies”, Solid State Technology, 63(6), pp.: 9528-9546.

49. The Times of India (2019), ‘Google Assistant is now available in eight more Indian languages’, The Times of India, 25 February 2019, https://timesofindia.indiatimes.com/ gadgets-news/google-assistant-is-now-available-in-four-more-indian-languages/article show/68152781.cms (accessed 11 Mar. 2020).

50. Trist, E. (1981), ‘The evolution of socio-technical systems’, Occasional paper, 2, https://stsroundtable.com/wp-content/uploads/ The-Evolution-of-Socio-Technical-Systems-Trist.pdf (accessed 10 Mar. 2020).

51. S. A. Kumar, A. Kumar, V. Dutt and R. Agrawal, "Multi Model Implementation on General Medicine Prediction with Quantum Neural Networks," 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2021, pp. 1391-1395, doi: 10.1109/ICICV50876.2021.9388575.

52. USAID (2019), ‘Artificial Intelligence in Global Health: Defining a Collective Path Forward’, https://www.usaid.gov/cii/ai-in-global-health (accessed 5 Mar. 2020).

https://the-ken.com/story/google-

https://analyticsindiamag.com/how-microsoft-is-

https://timesofindia.indiatimes.com/

https://stsroundtable.com/wp-content/uploads/

https://www.usaid.gov/cii/ai-in-global-health

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