title: Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles.
abstract: In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure. This ensemble approach has been applied to lateralization of focal epileptogenicity in temporal lobe epilepsy (TLE) and prediction of surgical outcomes. A goal of this study is to reduce extraoperative electrocorticography (eECoG) requirement which is the practice of using electrodes placed directly on the exposed surface of the brain. We have shown that such goal is achievable with application of data mining techniques. Furthermore, all TLE surgical operations do not result in complete relief from seizures and it is not always possible for human experts to identify such unsuccessful cases prior to surgery. This study demonstrates the capability of data mining techniques in prediction of undesirable outcome for a portion of such cases.
authors.affiliation: Dept. of Computer Science, Wayne State University, Detroit, MI, USA ; Image Analysis Lab., Dept. of Radiology, Henry Ford Health System, Detroit, MI, USA.
authors.forename: Shobeir
authors.initials: S
authors.lastname: Fakhraei
authors.affiliation: Image Analysis Lab., Dept. of Radiology, Henry Ford Health System, Detroit, MI, USA ; CIPCE, School of Elec. and Comp. Eng., University of Tehran, Tehran, Iran.
authors.forename: Hamid
authors.initials: H
authors.lastname: Soltanian-Zadeh
authors.affiliation: Image Analysis Lab., Dept. of Radiology, Henry Ford Health System, Detroit, MI, USA.
authors.forename: Kourosh
authors.initials: K
authors.lastname: Jafari-Khouzani
authors.affiliation: Dept. of Neurosurgery, Henry Ford Health System, Detroit, MI, USA.
authors.forename: Kost
authors.initials: K
authors.lastname: Elisevich
authors.affiliation: Dept. of Computer Science, Wayne State University, Detroit, MI, USA.
authors.forename: Farshad
authors.initials: F
authors.lastname: Fotouhi
keywords: AUC; Classification; Confidence-based Classification; Confident Prediction; Ensemble Methods; Epilepsy; Lateralization; Outcome; Performance Evaluation; Temporal Lobe