The study found that machine learning techniques improved diet prediction by 10-20%. When it comes to studying food and diet, it’s difficult to know what people are eating — let alone their risk of disease caused by what they eat.
Doctors and researchers usually ask people to fill out a long-from food frequency questionnaire that estimates caloric intake, food groups and nutrients. That relies on a person’s memory and may not provide the most accurate picture.
However, a research team led by a Michigan Medicine cardiologist have found a method using molecular profiling and machine learning to develop blood-based dietary signatures that more accurately predict both diet and the risk of cardiovascular disease and type 2 diabetes...
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