Long-Term Forecasting the Survival in Liver Transplantation Using Multilayer Perceptron Networks
研究利用多层感知器人工神经网络模型，基于美国器官共享联合网络数据库的13年随访数据，预测肝移植患者的长期生存率，模型在特异性、敏感性和准确性上优于现有方法。
Medical prognosis has become an emerging area in health care. Several reliable prognostic models based on survival analysis procedures have been useful to a variety of domains, with different degree of success. An enhanced model with further advancement of computerized medical decision support systems help clinicians and doctors in building strategies for understanding surgical outcomes. A tenfold cross validation (CV) was applied in the medical input dataset which was obtained from United Network Organ Sharing database. In order to perform the dimensionality reduction of a huge database, principal component analysis (PCA) with ranking was done. The relation between attributes was recognized and proved using various association rule mining techniques, such as apriori, tertius, and treap algorithms. For confirming the results, we compared the rules generated by the association rules mining algorithm before and after PCA was also performed. We proposed an effective and accurate artificial neural network (ANN) model for the prediction of long-term survival of liver patients who undergo liver transplantation (LT). A tenfold CV was applied in the medical input dataset which was obtained from United Network Organ Sharing database. We made survival analysis of 13 years in the prediction of liver patients after LT. We trained the liver follow up data of 13 years separately using multilayer perceptron ANN model with proper selection of data attributes. Our model outperformed the existing models in terms of specificity, sensitivity, and accuracy. From the comparison of existing approaches, our model promised high accuracy in survival analysis after LT. We have also evaluated the survival probabilities of 13 years of follow up liver data and proved that our prediction model is suitable for long-term prognosis of survival of patients after LT.