Diagnosis of illness via big data

Big data is defined as high volume, high variety/complexity, and high velocity, which is known as the 3Vs (Services, 2015). Using Machine Learning and Artificial Intelligence, they do well at analyzing patterns from frequent and voluminous amounts of data at faster speeds than humans, but they fail to recognize patterns in infrequent and small amounts of data (Goldbloom, 2016). Thus, the use of data analytic theories and techniques on big data rather than novel situations in healthcare is vital to understand.

Fayyad, Piatetsky-Shapiro, and Smyth (1996) defined that data analytics can be divided into descriptive and predictive analytics. Vardarlier and Silaharoglu (2016) agreed with Fayyad et al. (1996) division but added prescriptive analytics.  Thus, these three divisions of big data analytics are:

  • Descriptive analytics explains “What happened?”
  • Predictive analytics explains “What will happen?”
  • Prescriptive analytics explains “Why will it happen?”

(Fayyad et al., 1996; Vardarlier & Silahtaroglu, 2016). Depending on the goal of diagnosing illnesses with the use of big data analytics should depend on the theory/division one should choose.  Raghupathi & Raghupathi (2014), stated some common examples of big data in the healthcare field to be: personal medical records, radiology images, clinical trial data, 3D imaging, human genomic data, population genomic data, biometric sensor reading, x-ray films, scripts, and traditional paper files.

The use of big data analytics to understand the 23 pairs of chromosomes that are the building blocks for people. Healthcare professionals are using the big data generated from our genomic code to help predict which illnesses a person could get (Services, 2013). Thus, using predictive analytics tools and algorithms like decision trees would be of some use.  Another use of predictive analytics and machine learning can be applied to diagnosing an eye disease like diabetic retinopathy from an image by using classification algorithms (Goldbloom, 2016).

The study of epigenetics, which are what parts of the genetic code is turned on versus turned off, can help explain why will certain illnesses are more probable to occur in the future (What is epigenetics, n.d.).  Thus, the use of prescriptive analytics could be of some use in the study of epigenetics. Currently, clinical trials use descriptive analytics to help calculate true positives, false positives, true negatives, and false negatives of a drug treatment versus a placebo are commonly used.  Thus, depending on the goal of diagnosing illnesses and the problem, that should help define which theories and techniques of big data analytics to use. The use of different data analytics techniques and theories based on the problem and data can change how healthcare jobs in the next 30 years from today (Goldbloom, 2016; McAfee, 2013).


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