期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：2
出版社：S&S Publications
摘要：Failed Back Surgery Syndrome (or FBSS) refers to patients with persistent or new pain after spinal surgery for back or leg pain. Multiple factors can contribu te to its onset. We studied different techniques of data mining to determine the prognosis of patients with FBSS . Different machine learning algorithms are tested to find the best algorithm that predicts the factors that influence FBSS from the set of 305 patients operated for lumbar disc herniation . Since the data is unbalanced other criteria rather than accuracy is used as the evaluation criteria. The tools that are developed using WEKA API and the approach of feature selection test different machin e learning algorithms to evaluate the best algorithm that maximize AUC or F - Measure, but on the same time maintaining false negative low. The results of the experi ments are discussed and the factors that mostly influence on this syndrome are identified.
关键词：AUC; Fmeasure; data mining; failed back surgery syndrome; prognosis
