Download 13th International Conference on Cochlear Implants and Other

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Transcript
S38-2
Preimplantational evaluation: Prognosis estimation by data mining system
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Guerra Jiménez G. , Ramos De Miguel Á. , Falcón González J.C. , Borkoski Barreiro S. , Ramos Macías Á.
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CHUIMI, ORL, Las Palmas de GC, Spain, 2CHUIMI, Las Palmas de GC, Spain
Introduction: Prediction of speech recognition and quality of life (QoL) outcome after a cochlear implantation
(CI) is still one of the most important challenges for otologysts.
Data mining (DM) is an interdisciplinary subfield of computer science. DM uses artificial intelligence techniques,
neural networks, and advanced statistical tools to reveal trends, patterns and relationships which might
otherwise have remained undetected. There are identifiable preimplantational factors such as profound
hypoacusis duration, age at the moment of cochlear implantation and sociodemographic or educational factors
that condicion CI outcome. So an objective initial analysis of multiple known variables could allow us to predict CI
benefits. Our objective is to design a DM system to predict and classify in each patient the CI predictable
benefits in terms of speech recognition and QoL.
Material and methods: Observational study of 29 adults, randomly selected, CI users during at least one year.
Audiological benefits and its relation to QoL are analyzed using two specific questionnaires: the Glasgow Benefit
Inventory (GBI) and the Hearing Aids Specific Questionnaire (HASQ). Data is recorded in SPSS Statistics 19.0,
and MatLab, and then processed in Weka system. By Nearest Neighbor, Decision Tree algorithms and logistic
regression, classifiers and estimators are designed so, based on preimplantational attributes, they indicate
postimplantational predictable outcome.
Results: Clasifiers. By Nearest Neighbor, selecting the best algorithms (IB1), the interesting attributes to classify
speech recognition in words are category (unilateral CI vs bimodal), voice identification in noisye embironment
and being able to use the phone usebefore CI (HASQ), (with a success percentage of 80,7%). Decision tree J48
showed that influencing variables for GBI are the marital status, living situation, age at the moment of the hearing
impairment and the previous use of hearing aids, with a success of 81%. For HASQ, J48 selected as the
influencing variables are education level, voice recognition at phone and HASQ index before CI, with a success
percentage of 85%. Estimators, disclose a precision of 85%, 68.43% and 71,16% for words perception, GBI and
HASQ respectively.
Discussion: DM has been used in market analysis to identify new product bundles, , prevent customer attrition
and acquire new ones, cross-sell to existing customers and even identify the features of their most successful
employees. In medical field, DM has contributed in decision making and problem solving like detecting drugs´
quantitative adverse effects or predicting stroke's risk. In otoneurology, DM has predicted hearing impairment in
children, profile Meniere´s disease patients or foretell its evolution.
Conclusion: Our study propose new system to classify and estimate speech perception outome and QoL
improvement based on our first clinical evaluation in order to decision making and patient’s information.
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