Artificial neural networks predict the incidence of deep venous thrombosis in hospitalized patient

Abstract Objective: To construct and validate artificial neural networks (ANNs) for predicting the occurrence of deep venous thrombosis(DVT) and compare the predictive performance of the ANNs model with that of logistic regression(LR)model,linear discriminant analysis(LDA) model,and simple artificial neural network (SANNs) model. Methods: 1295 cases were selected, including 729 patients with DVT and 566 patients without. 75% of the cases (993 cases) are randomly selected as the training set for model construction, and the remaining 25% of the cases (302 cases) are used as the testing set to verify the prediction performance. After deep learning of the training data, the ANNs model with different numbers of hidden_nodes and epochs was constructed. The prediction efficiency of the ANNs model was tested by comparing the results of LR,LDA,and SANNs model as the benchmark afterwards. Results: When the number of hidden_nodes was 8 and the number of epochs was 800 in ANNS model, the Acc reached the highest,which the Acc, Youden index was 81.84%, 0.6450 respectively.The prediction performance of this model was higher than that of LR,LDA ,and SANNs. Conclusions: This study provided good evidence for the application of ANNs to predict DVT in a large number of data. However, more research will be needed to confirm its application in the prediction of DVT.