Case Study: “Crystal Ball, Clairvoyant, Fortune Telling…Can Predictive Analytics Deliver the Future?” (Closing Case One in Ch 4 of the textbook: Haag & Cummings). Discuss the case study here in this forum.¬† In your initial post, address the following:
1. Provide short overview of case and include key components.

2. Many predictive analytic models are based on neural network technologies. What is the role of neural networks in predictive analytics? How can neural networks help predict the likelihood of future events? In answering these questions, specifically reference Blue Cross Blue Shield of Tennessee.

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The role of Neural networks in predictive analytics involves identification, classification, and prediction when a vast amount of information is available. It is also useful for finding and differentiating patterns. By examining hundreds, or even thousands of examples, a neural network detects important relationships and patterns in the information (Haag & Cummings, 2013). By understanding current or historical patterns or data, such data can then be used to determine the likelihood of future occurrence.

By analyzing the information on current and past postoperative patients, Blue Cross Blue Shield’s neural network can help to determine what resources will be needed by and most suitable for future patients.

3. What if the Richmond police began to add demographic data to its predictive analytics system to further attempt to determine the type of person (by demographic) who would in all likelihood commit a crime. Is predicting the type of person who would commit a crime by demographic (ethnicity, gender, income level, and so on) good or bad?
Using demographic information in predicting the possibility of one committing crime could be useful in some situations. However, in recent times there has been increased sensitivity to the issues of gender, race and ethnicity. Therefore, law enforcement developing systems that correlate such factors when looking out for potential criminals may spark up huge controversies.
4. In the movie¬†Gattaca, predictive analytics were used to determine the most successful career for a person. Based on DNA information, the system determined whether or not an individual was able to advance through an educational track to become something like an engineer or if the person should only complete a lower level of education and become a janitor. The government then acted on the system’s recommendations and placed people in various career tracks. Is this a good or bad use of technology? How is this different from the variety of personal tests you can take that inform of your aptitude for different careers?
In my opinion, this is bad use of technology. According to the nature-nurture debate within psychology, the individual’s characteristics are not solely determined by their genetic composition (DNA) but also by interaction with the environment from childhood to adulthood.
Personality tests, also a predictive system, often contain series of questions that inquire about different aspects of the individual’s characteristics. Personality is stable overtime and is a more dependable measure for predicting future outcomes
5. What role can geographic information systems (GISs) play in the use of predictive analytics? As you answer this question, specifically reference FedEx’s use of predictive analytics to (1) determine which customers will not respond positively to a price increase and (2) project additional revenues from proposed drop-box locations.

6. The Department of Defense (DoD) and the Pacific Northwest National Laboratory are combining predictive analytics with visualization technologies to predict the probability that a terrorist attack will occur. For example, terrorists caught on security cameras who loiter too long in a given place might signal their intent to carry out a terrorist attack. How can this type of predictive analytics be used in an airport? At what other buildings and structures might this be used?
7. References