Prognosis of Failed Back Surgery Syndrome Based On Feature Extraction Method | Abstract

ISSN ONLINE(2320-9801) PRINT (2320-9798)

Research Article Open Access

Prognosis of Failed Back Surgery Syndrome Based On Feature Extraction Method

Abstract

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 contribute 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 machine 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 experiments are discussed and the factors that mostly influence on this syndrome are identified.

Alda Kika, Ridvan Alimehmeti

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