A new study suggests type 2 diabetes can be predicted years in advance by a machine learning model that can scour and analyze routinely collected health data for a variety of risk factors.
Lead author Laura Rosella, a scientist at the non-profit research group I-C-E-S, says researchers looked at health information as well as social and demographic data from a random sample of nearly 1.7 million residents in Ontario between 2006 and 2016.
The machine learning model considered variables including high blood pressure and body mass index to predict who was at highest risk of developing type 2 diabetes five years later.
It turned out to be 80 per cent accurate, finding very high-risk patients to be 58-years-old on average, including a greater proportion of immigrants, and people who were more likely to live in neighbourhoods with lower incomes and higher unemployment.
The goal of the study was not to guide individual treatment.
Researchers say the data can help develop targeted population-wide strategies to reduce disease prevalence among high risk groups, ease the burden on the health-care system and save millions of dollars in costs related to diabetes.
The study was published Tuesday in the journal JAMA Network Open.
This report by The Canadian Press was first published May 25, 2021.
Camille Bains, The Canadian Press