Use of data mining to predict significant factors and benefits of bilateral cochlear implantation

[1]  J. Niparko,et al.  Electric Charge Requirements of Pediatric Cochlear Implant Recipients Enrolled in the Childhood Development After Cochlear Implantation Study , 2008, Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology.

[2]  T. Lenarz,et al.  The benefits of sequential bilateral cochlear implantation for hearing-impaired children , 2008, Acta oto-laryngologica.

[3]  R. V. Hoesel.  Exploring the benefits of bilateral cochlear implants. , 2004 .

[4]  Wolfgang Gaul,et al.  "Classification, Clustering, and Data Mining Applications" , 2004 .

[5]  F. Guillén-Grima,et al.  Cluster analysis of auditory and vestibular test results in definite menière's disease , 2011, The Laryngoscope.

[6]  C. Schreiner,et al.  Sensory input directs spatial and temporal plasticity in primary auditory cortex. , 2001, Journal of neurophysiology.

[7]  Stuart Gatehouse,et al.  Glasgow Hearing Aid Benefit Profile: Derivation and Validation of a Client-centered Outcome Measure for Hearing Aid Services , 1999 .

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  W. Parkinson,et al.  Residual speech recognition and cochlear implant performance: effects of implantation criteria. , 1999, The American journal of otology.

[10]  L. Kim,et al.  Bilateral cochlear implants in children , 2009, Cochlear implants international.

[11]  Donald Robertson,et al.  Plasticity of frequency organization in auditory cortex of guinea pigs with partial unilateral deafness , 1989, The Journal of comparative neurology.

[12]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[13]  Zalinda Othman,et al.  Using rough set theory for mining the level of hearing loss diagnosis knowledge , 2009, 2009 International Conference on Electrical Engineering and Informatics.