MITS6002 Business Analytics Assignment 2 Research Study MITS6002 Assignment 2 Copyright © XXXXXXXXXXVIT, All Rights Reserved. 2 Learning Outcomes The following learning outcomes have been covered in...

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Contents
Introduction    3
Use of predictive analysis in healthcare industry    3
Heathcare in digital era    4
The importance of the predictive analytics in healthcare    5
Moral and Ethics hazards in predictive analytics for the healthcare sector    7
Conclusion    8
Introduction
Predictive analysis has not completely changed. It is applying what doctors have been doing on a large scale. The changes that have taken place are our ability to better measure, aggregate and understand previously unavailable or non-existent behavioral, psychological and biostatistical data. Combining these new data sets with the existing science of epidemiology and clinical medicine enables us to accelerate the understanding of the relationship between external factors and human biology, which ultimately enhances the redesign of clinical pathways and true personality Care. As predictive analytics becomes more and more useful in operations management, personal medicine and epidemiology, technology plays an indispensable role in global healthcare. As society continues to enter an innovative era of the decision-making, government medical institutions, doctors or primary health care providers require being alert of emerging threats and reaching a consensus on the level of assurance, supplemented by evidence of digital technology and sometimes even replaced (Bhatia and Sood, 2017).
Use of predictive analysis in healthcare industry
Predictive analysis can be described as a branch of advanced analysis that can be used to predict unidentified future activities or events that can lead to the decisions. It’s a regulation that uses various technologies, including modeling, statistics and data mining, and AI (for example machine learning) to evaluate real-time and historical data moreover predict the future. These predictions provide a different opportunity to observe the future at the individual level and cohort size and determine future trends in patient care (Blasenak, 2019).
Predictive analysis is based on logic based on theories developed by humans to adapt to assumptions (supervised learning). Predictive analysis can also be based on unguided learning, which has no guiding assumptions, but uses an algorithm to find patterns or structures in the data and group or group them. In unsupervised learning, machines might not understand what to look for, but when processing data, machines will begin to recognize complex patterns and processes that humans might never have recognized, so they can add important value to researchers looking for new things. Both unsupervised and supervised predictive modeling is effective analytical tools that can be used in the full application of these technologies (Borade, Dsouza, Munde and Varghese, 2019).
The application of predictive analysis is increasing, and it is very helpful in several companies such as manufacturing, law, marketing, crime, healthcare, and fraud detection. The healthcare sector with several stakeholders will be the main beneficiary of the predictive analytics, and advanced technology is identifed as an integral part of medical service delivery.
Heathcare in digital era
In recent years, the two most destructive factors are the increase of smartphones and Internet. They are together so that people around the whole world can access a lot of data and knowledge at their fingertips. These have changed the industry, including arguably the strictly regulated or traditional industry, namely healthcare, which is undergoing tremendous changes.
Some main milestones include the digitization of wellbeing records, access to the storage and big data in the cloud, mobile application technologies and advanced software. All of these milestones demonstrate several benefits in the healthcare field, including streamlining workflow, accessing information faster, reducing health care costs, improving public health, as well as improving overall quality of life. They also help importantly reduce healthcare waste, develop new medicines and new therapies, and avoid preventable deaths (Business Intelligence in Healthcare Industry, 2015).
The risks of predictive analysis include the concentration of data, which brings huge risks in terms of data security and integrity. Considering the rising amount of information that is usually stored in accessible or cloud through the Internet, there are continuous threats that may be perpetrated by individuals for malicious purposes. Considering the role of cloud technology in predictive analysis and overall results, there are some ethical issues to consider (Grissette, Nfaoui and Bahir, 2017).
The importance of the predictive analytics in healthcare
In order to better know the several...