Cochlear Implant Evaluation: Prognosis Estimation by Data Mining System | Zendy

Gloria GuerraJiménez | Zendy; Ángel Ramos de Miguel | Zendy; Juan Carlos Falcón González | Zendy; Silvia A. Borkoski Barreiro | Zendy; Daniel Pérez Plasencia | Zendy; Ángel Ramos Macías | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Cochlear Implant Evaluation: Prognosis Estimation by Data Mining System

Author(s) -

Gloria GuerraJiménez,

Ángel Ramos de Miguel,

Juan Carlos Falcón González,

Silvia A. Borkoski Barreiro,

Daniel Pérez Plasencia,

Ángel Ramos Macías

Publication year - 2016

Publication title -

the journal of international advanced otology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.518

H-Index - 13

eISSN - 2148-3817

pISSN - 1308-7649

DOI - 10.5152/iao.2016.510

Subject(s) - c4.5 algorithm , logistic regression , decision tree , medicine , estimator , cochlear implant , observational study , statistics , data mining , machine learning , audiology , computer science , support vector machine , mathematics , naive bayes classifier

Prediction of speech recognition (SR) and quality of life (QoL) outcomes after cochlear implantation is one of the most important challenges for otologists. By sifting through very large amounts of data, data mining reveals trends, patterns, and relationships that might otherwise have remained undetected. There are identifiable pre-implantational factors that condition the cochlear implantation outcome. Our objective is to design a data mining system to predict and classify cochlear implant (CI) predictable benefits in terms of SR and QoL in each patient.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research