A Neural Network Based on Rough Set (RSNN) for Prediction of Solitary Pulmonary Nodules

Although algorithms based on rough set (RS) theory can extract useful decision rules with the effectiveness in dealing with inexact, uncertain or vague information, the deterministic mechanism for the description of error is very simple and the rules generated by RS are often unstable and have low classification accuracy. Neural networks (NN) are considered the most powerful classifier for their low classification error rates and robustness to noise. But NN usually require long time to train the huge amount of data of large databases and lack explanation facilities for their knowledge. Therefore, we combine RS and NN for autonomous decision-making, with high accuracy, robustness to noise, efficiency, and good understandability. First, generate the decision rules based on RS, then construct the NN with the hidden layer representing decision rules, and learn the arguments of the NN with BP algorithm. With the direction of RS, NN neednpsilat long time for training, and the knowledge buried in their structures and weights can be well explained by decision rule on RS. The proposed algorithm has been tested on a medical data set for patients with solitary pulmonary nodules (SPN).