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Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50-80% proportion of T2DM is undiagnosed. In this paper, support vector machines are utilized to screen diabetes, and an ensemble learning module is added, which turns the “black box” of SVM decisions into comprehensible and transparent(More)
AIMS To develop a risk assessment model for persons at risk from type 2 diabetes in Chinese. MATERIALS AND METHODS The model was generated from the cross-sectional data of 16246 persons aged from 20 years old and over. C4.5 algorithm and multivariate logistic regression were used for variable selection. Relative risk value combined with expert decision(More)
A simulation based computational method was conducted to reflect the effect of intervention for those at high risk of type 2 diabetes. Hierarchy Support Vector Machines (H-SVMs) were used to classify high risk. The proportion transitioning from the high risk state to moderate state, low state or the normal state was calculated. When Body Mass Index (BMI)(More)
River systems are valuable to human beings; meanwhile, they are intensively influenced by human activities, especially urbanization. In this study, based on the data derived from topographic maps and remote sensing images, the temporal and spatial change of river system geomorphology in the Taihu Region over the past 50 years was investigated in conjunction(More)
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