A new artificial intelligence tool can predict the size of a person's waistline by simply analyzing their age, height, weight, ethnicity, and level of education, Johns Hopkins University engineers have found. The tool's striking accuracy could help doctors estimate a patient's risk of diabetes, heart disease, stroke, and other obesity-related conditions.
They applied a machine learning technique, called conformal prediction, to predict waist circumference. Along with the prediction, their model produces a range of values that expresses the model's confidence in the prediction's accuracy.
On top of that, the authors show that the uncertainty ranges were reliable and generalizable, meaning the model can make accurate predictions about populations that differ substantially from those the model was trained on, such as patients with diabetes. This adds a layer of safety and accuracy, especially in clinical settings where such uncertainty is critical and guides decision-making.
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