Fuzzy Rule Extraction from Nursing-Care Texts
Manabu Nii, Takafumi Yamaguchi, Yutaka Takahashi, Atsuko Uchinuno, Reiko Sakashita · 2009
The nursing care quality improvement is very important for our life. Currently, nursing-care freestyle texts (nursing-care data) are collected from many hospitals in Japan by using Web applications. The collected nursing-care data are stored into the database. To evaluate nursing-care data, we have already proposed a fuzzy classification system, a neural network based system, a support vector machine (SVM) based classification system. Then, in order to improve the classification performance, we have proposed a genetic algorithm (GA) based feature selection method for generating numerical data from collected nursing-care texts.In this paper, we propose a fuzzy rule extraction method from the nursing-care text data. First, features of nursing-care texts are selected by a genetic algorithm based feature selection method. Next, numerical training data are generated by using selected features. Then we train neural networks using generated training data. Finally, fuzzy if-then rules are extracted from the trained neural networks by the parallelized rule extraction method.From computer simulation results, we show the effectiveness of our proposed method.