Predictive analytics works by using logic to match a hypothesis, known as supervised learning. Predictive analytics can also be based on unsupervised learning, meaning there is no guiding hypothesis. It then uses an algorithm to find patterns and data structure and combine them into groups or insights. In unsupervised learning, the machine doesn’t quite know what it is looking for, but as it processes the data, it begins to identify complex processes and certain patterns that a human could never decipher on their own. This means that such activity can yield valuable insights for researchers. Therefore both types i.e. supervised and unsupervised predictive modeling are important analytical tools used in many fields.

Application areas of predictive analytics

Data collection is the basis for performing any function in the area of artificial intelligence, machine learning, etc. Nowadays, information gathering is becoming more and more popular. This situation leads to predictive analytics becoming both more common and more accurate. Currently, many predictive models use traditional statistical methods such as logistic regression, which can provide insightful results. However, artificial intelligence and machine learning methods can provide more accurate predictions. Predictive analytics supports healthcare systems in transforming to an individual approach, which improves care in terms of quality, efficiency, cost, and patient satisfaction. In the healthcare system, we can list the key areas where predictive analytics is applicable:

  • diagnoses,
  • prognosis,
  • treatment course design,
  • clinical decision support,
  • remote monitoring,
  • reduction of adverse events,
  • reduction of healthcare costs,
  • improvement of quality of care.

Read more: Collaboration with University College Leuven Limburg – “ASTMApping”

The problem of chronic disease

Managing chronic diseases is difficult because their management involves constant monitoring of patients’ health. This may be very inconvenient for people who have many other responsibilities on top of their illness. As a result, many individuals neglect the therapy which can significantly affect their health. As the global population continues to grow, the need to track the overall well-being and health of the population rises to prevent the development of chronic diseases. Today, healthcare providers can use predictive analytics that rely on artificial intelligence to manage population health. An example of using predictive analytics is predictive risk assessment. This activity relies on electronic medical records, laboratory tests, biometric data, and social determinants, among others, to gain insights into the health of a population. It may be said that big data and predictive analytics in healthcare go hand in hand.

IoT data from wireless devices and predictive analytics

IoT predictive analytics is a bit different from traditional methods of collecting and using data. These sensors can generate and collect a very wide range of data. The FindAir ONE device created by FindAir is an example of an IoT device. It collects data about each use of the inhaler with the number of doses, place, time, and real-time circumstances. Depending on the type of device, such data sets can be updated regularly, sometimes every minute or second. Today, data is driving growth and development in IT products and services. We witness improvements in the way companies collect, understand, and use data and have started to realize more proactive and predictive solutions.

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