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This paper presents and compares soft computing approaches for prediction of surgery outcome in temporal lobe epilepsy. Because of a wide range of effective parameters in epilepsy and unclear exact contribution of each, determination of the best treatment is difficult. We have implemented and compared data fusion methods and decision support algorithms to overcome this difficulty. Our simulation studies and experimental results using HBIDS (human brain image database system) data show the power of LS-SVM (least squared support vector machine) classifiers for this purpose.