Machine Learning Used to Predict Risk of People Developing Type 2 Diabetes

Machine learning could be used to predict which Canadians are at risk of developing Type 2 diabetes, say University of Toronto researchers.

The discovery is important because it can be used to make health policy decisions that could improve the lives of millions of Canadians.

“We know that identifying people who are at risk of developing Type 2 diabetes is really important because there are things we can do to prevent the onset of the disease,” says Laura Rosella, an associate professor at Temerty Faculty of Medicine and Dalla Lana School of Public Health.

“This machine learning model can help with managing one of the biggest chronic disease challenges in North American society. There is a demonstrated advantage to intervening early when people are at risk of Type 2 diabetes.”

Rosella is the senior author on the study, which was recently published in JAMA Network Open. The study used a machine learning model that analyzed health data of 2.1 million people living in Ontario, that had been collected from 2006 to 2016.

Researchers found that they were able to use the model to accurately predict the number of people who would develop Type 2 diabetes within a five-year time period.
The machine learning model was also able to analyze different factors that would influence if people were high risk or low risk to develop the disease.

Rosella, the education lead for the Temerty Centre for AI in Medicine (TCAIREM), says the findings could help inform larger health system strategies to decrease the number of people who develop of Type 2 diabetes.

“The model is about 80 per cent accurate when it comes to predicting who will develop Type 2 diabetes,” she says.
“By using this information in a pro-active way, we can plan health systems better and help prevent what can be a serious, burdensome condition.”

The use of a machine learning model is important, says Rosella.

That’s because it shows how routinely collected data can be used to address complex health problems in a more effective way, says Rosella.

Preventing Type 2 diabetes means looking at larger structural factors like food insecurity and access to primary care physicians, says Rosella.

“We know diabetes can be prevented or delayed. We know there are effective ways we can prevent the onset of a chronic disease. This study offers a way to start thinking about who to identify who is at risk of Type 2 diabetes, and then start implementing strategies to stop the onset of a debilitating, lifelong condition,” she says.

Vinyas Harish, an MD-PhD candidate at Temerty Faculty of Medicine and learner co-lead at TCAIREM, says the research illuminates how scrutinizing social determinants of health have an important impact on stopping the spread of Type 2 diabetes.

“It helps us think about what we can do to get a health system to intervene on larger, more structural factors,” he says.

Rosella says medical research that incorporates artificial intelligence requires a team approach.

“You need a multi-disciplinary group of people that include a group of really good computer scientists, people that understand data and how to use it, and people with a health system perspective and a clinical perspective,” she says.
“This is needed to make sure that you’re coming up with algorithms that are actually going to be used and have an impact.”