Objectives: Observational studies suggested that patients with type 2 diabetes mellitus\n(T2DM) presented a higher risk of developing colorectal cancer (CRC). The current study aims\nto create a deep neural network (DNN) to predict the onset of CRC for patients with T2DM.\nMethods: We employed the national health insurance database of Taiwan to create predictive\nmodels for detecting an increased risk of subsequent CRC development in T2DM patients in Taiwan.\nWe identified a total of 1,349,640 patients between 2000 and 2012 with newly diagnosed T2DM.\nAll the available possible risk factors for CRC were also included in the analyses. The data were split\ninto training and test sets with 97.5% of the patients in the training set and 2.5% of the patients in\nthe test set. The deep neural network (DNN) model was optimized using Adam with Nesterovâ??s\naccelerated gradient descent. The recall, precision, F1 values, and the area under the receiver\noperating characteristic (ROC) curve were used to evaluate predictor performance. Results: The F1,\nprecision, and recall values of the DNN model across all data were 0.931, 0.982, and 0.889, respectively.\nThe area under the ROC curve of the DNN model across all data was 0.738, compared to the ideal\nvalue of 1. The metrics indicate that the DNN model appropriately predicted CRC. In contrast,\na single variable predictor using adapted the Diabetes Complication Severity Index showed poorer\nperformance compared to the DNN model. Conclusions: Our results indicated that the DNN model\nis an appropriate tool to predict CRC risk in patients with T2DM in Taiwan.
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