What is Machine Learning?
Machine Learning, or ML, is used to describe algorithms that are able to collect and analyse data in order to more accurately predict outcomes, without having been explicitly programmed to do so.
Although science fiction might paint it a little differently, machine learning is actually used in a variety of settings to pick up on trends of all kinds, making it commonplace on the sites and apps that most of us use daily. One of the easiest and most well-known examples to explain Machine Learning in action is Facebook’s News Feed function, as its functionality has become standard on other platforms, including Facebook’s sister app Instagram, YouTube and Twitter too.
What can Machine Learning do?
Machine Learning includes these processes and more to suggest outcomes, with its accuracy improving as it receives and compares new data. In the case of Facebook, its News Feed uses machine learning to track what content its users engage with, how long they spend engaging with a post and who created the post in order to compare with others who have responded similarly to this post and others like it. This allows Facebook to cater a user’s experience to the profiles, pages and topics that its existing data indicates would be most relevant. Facebook’s News Feed uses this behaviour tracking to ensure that the content it believes will be most relevant, and likely to be engaged with, at the top of a user’s feed.
Other examples of mainstream use include Gmail’s priority inbox function, Amazon’s homepage recommendations and it features prominently within the development of self-driving cars. Although every Machine Learning process does require some initial “training” while it builds the volume of data that will improve its responsiveness, its functions will continue to become more complex and significant as time goes on.
Machine Learning in Healthcare
Not only beneficial in the private sector, Machine Learning is also demonstrating its value within healthcare through its ability to analyse probable outcomes, while also helping to predict costs and timescales in given scenarios.
This probable outcome analysis is achieved through the use of classification algorithms, using scores to rate the likelihoods of various scenarios, like whether a patient has a certain condition based on the symptoms that have been logged.
The prediction of continuous values is carried out by regressive algorithms, which can offer healthcare professionals indications on is how much procedures will cost, how many patients are likely to be admitted during one day, or how many staff members are required to cover a ward at any given point in time.
Machine Learning is already proving itself to be a powerful way to give more accurate data to medical staff allowing for better-informed decisionmaking on every level of their industry; it is sure to be an exciting step forward in supporting healthcare professionals in years to come.