Cochlear Implant Evaluation: Prognosis Estimation by Data Mining System
| Autor: | Borkoski Barreiro Sa, Pérez Plasencia D, Ramos Macías A, Guerra-Jiménez G, Ramos De Miguel Á, Falcón González Jc |
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| Rok vydání: | 2016 |
| Předmět: |
Male
medicine.medical_specialty medicine.medical_treatment Decision tree Deafness Audiology computer.software_genre Logistic regression 03 medical and health sciences 0302 clinical medicine C4.5 algorithm Quality of life Surveys and Questionnaires Cochlear implant medicine Data Mining Humans 030223 otorhinolaryngology Speech Reception Threshold Test business.industry Decision tree learning Decision Trees Estimator General Medicine Middle Aged Prognosis Cochlear Implantation Otorhinolaryngology Patient Satisfaction Quality of Life Female Observational study Data mining business computer Algorithms 030217 neurology & neurosurgery |
| Zdroj: | The Journal of International Advanced Otology. 12:1-7 |
| ISSN: | 2148-3817 1308-7649 |
| DOI: | 10.5152/iao.2016.510 |
| Popis: | OBJECTIVE 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. MATERIALS AND METHODS This is an observational study of CI users for at least one year. Audiological benefits and its relation to QoL are analyzed using the Glasgow Benefit Inventory (GBI) and the Specific Questionnaire (SQ). Sociodemographic and medical variables are processed in SPSS Statistics 19.0, MatLab® and Weka®. Classifiers are designed using the nearest neighbour and decision tree algorithms. Estimators are created by linear logistic regression. RESULTS A total of 29 patients (mean age, 55.3 years; 52% female and 48% male) including 48% unilateral CI users and 51% bimodal CI users were included in the study. GBI improved by 36 points and SQ by 1.7 (p |
| Databáze: | OpenAIRE |
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