Background Diabetes mellitus is associated with an increased risk of liver cancer and these two diseases are among the most common and important causes of morbidity and mortality in Taiwan . Purpose To use data mining techniques to develop a model for predicting the development of liver cancer within 6 years of diagnosis with type II diabetes . Methods Data were obtained from the National Health Insurance Research Database of Taiwan which covers approximately 22 million people . In this study we selected patients who were newly diagnosed with type II diabetes during the 2000 2003 periods with no prior cancer diagnosis . We then used encrypted personal ID to perform data linkage with the cancer registry database to identify whether these patients were diagnosed with liver cancer . Finally we identified 2060 cases and assigned them to a case group and a control group . The risk factors were identified from the literature review and physicians suggestion then chi square test was conducted on each independent variable for a comparison between patients with liver cancer and those without those found to be significant were selected as the factors . We subsequently performed data training and testing to construct artificial neural network and logistic regression prediction models . The dataset was randomly divided into 2 groups a training group and a test group . The training group consisted of 1442 cases and the prediction model was developed on the basis of the training group . The remaining 30 were assigned to the test group for model validation . Results The following 10 variables were used to develop the ANN and LR models sex age alcoholic cirrhosis nonalcoholic cirrhosis alcoholic hepatitis viral hepatitis other types of chronic hepatitis alcoholic fatty liver disease other types of fatty liver disease and hyperlipidemia . The performance of the ANN was superior to that of LR according to the sensitivity specificity and the area under the receiver operating characteristic curve . After developing the optimal prediction model we base on this model to construct a web based application system for liver cancer prediction which can provide support to physicians during consults with diabetes patients . Conclusion In the original dataset 33 of diabetes patients were diagnosed with liver cancer . After using 70 of the original data to training the model and other 30 for testing the sensitivity and specificity of our model were 0.757 and 0.755 respectively this means that 75.7 of diabetes patients can be predicted correctly to receive a future liver cancer diagnosis and 75.5 can be predicted correctly to not be diagnosed with liver cancer . These results reveal that this model can be used as effective predictors of liver cancer for diabetes patients after discussion with physicians they also agreed that model can assist physicians to advise potential liver cancer patients and also helpful to decrease the future cost incurred upon cancer treatment . 


Summary: Developed a liver cancer prediction model for type II diabetic patients. Found the risk factors of liver cancer for type II diabetic patients. Built a web based application for liver cancer prediction for diabetic patient. Provided methods and model facilitating clinicians to remind those diabetic patients prevent liver cancer as early as possible.