TORONTO --
Canadian researchers have developed a machine-learning model that accurately predicts diabetes in a population using routinely collected health data, a new study says.
The study, published in the JAMA Network Open journal, tested new machine-learning technology on routinely collected health data that examined the entire population of Ontario. The study was run by the ICES not-for-profit data research institute.
Using linked administrative health data from Ontario from 2006 to 2016, researchers created a validated algorithm by training the model on information taken from nearly 1.7 million patients.
Using the validated data collected including variables like body mass index and high blood pressure, the algorithm was 80 per cent accurate in predicting who was at risk of type 2 diabetes in the population over several years.