In complex data environments, rational handling of unbalanced datasets is key to improving the reliability of early disease prediction. Early warning of disease risk in both temporal and spatial terms, contributes to disease prevention and treatment. To this end, a bi-dimensional substratum information mining model based on Association Rule Digging with Dynamic Thresholding and Weight Optimization (ARDdtwo) was proposed for the early diagnosis of thyroid cancer. It is an integrated assessment framework consisting of association rule digging by constructing a dynamic threshold model (ADRcdt) fo...
