Chronic disease risk assessment is a common information processing task performed by primary care physicians. However, efficiently and effectively integrating information about many risk factors across many patients is cognitively difficult. Methods for visualizing multidimensional data may augment risk assessment by providing reduced-dimensional displays which classify patient data. This study develops a framework which combines medical evidence, statistical dimensionality reduction techniques, and information visualization to develop visual classifiers for the task of diabetes risk assessment in a population of patients. The framework is evaluated in terms of classification accuracy and medical interpretation for two case studies, prediction of type 2 diabetes onset and prediction of heart attacks in adults with type 2 diabetes.
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