A diabetes risk early-warning system. Said system comprises: a storage device; and a first processor (1) based on improved k-means clustering, the first processor being coupled to the storage device and configured to: select a first clustering center point; and obtaining each stable cluster center on the basis of the first clustering center point, and substituting same into a diabetes piecewise function to obtain an early-warning model of diabetes, wherein a data set is selected, the number k of clustering clusters and a domain radius ε are defined, and a point with the maximum sum of distances between a sample point Xi and a sample is selected as a first clustering center point, so that the first clustering center point falls at the center of each cluster. The system improves a clustering center method, establishes a diabetes piecewise function early-warning model, improves the diabetes early-warning capacity, and provides a basis for the diagnosis and treatment of diabetes at different stages. In addition, starting from the features of a diabetes data set, key feature variables of diabetes are filtered and selected, so that a diabetes prediction model is simplified, and the accuracy of the diabetes prediction model is improved, thereby helping to provide accurate diabetes prevention and treatment measures.
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