Prognosis Prediction of Sudden Sensorineural Hearing Loss Using Ensemble Artificial Intelligence Learning Models.
Otol Neurotol
; 45(7): 759-764, 2024 Aug 01.
Article
en En
| MEDLINE
| ID: mdl-38918073
ABSTRACT
OBJECTIVE:
We used simple variables to construct prognostic prediction ensemble learning models for patients with sudden sensorineural hearing loss (SSNHL). STUDYDESIGN:
Retrospectively study.SETTING:
Tertiary medical center. PATIENTS 1,572 patients with SSNHL. INTERVENTION Prognostic. MAIN OUTCOMEMEASURES:
We selected four variables, namely, age, days after onset of hearing loss, vertigo, and type of hearing loss. We also compared the accuracy between different ensemble learning models based on the boosting, bagging, AdaBoost, and stacking algorithms.RESULTS:
We enrolled 1,572 patients with SSNHL; 73.5% of them showed improving and 26.5% did not. Significant between-group differences were noted in terms of age ( p = 0.011), days after onset of hearing loss ( p < 0.001), and concurrent vertigo ( p < 0.001), indicating that the patients who showed improving to treatment were younger and had fewer days after onset and fewer vertigo symptoms. Among ensemble learning models, the AdaBoost algorithm, compared with the other algorithms, achieved higher accuracy (82.89%), higher precision (86.66%), a higher F1 score (89.20), and a larger area under the receiver operating characteristics curve (0.79), as indicated by test results of a dataset with 10 independent runs. Furthermore, Gini scores indicated that age and days after onset are two key parameters of the predictive model.CONCLUSIONS:
The AdaBoost model is an effective model for predicting SSNHL. The use of simple parameters can increase its practicality and applicability in remote medical care. Moreover, age may be a key factor influencing prognosis.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Pérdida Auditiva Súbita
/
Pérdida Auditiva Sensorineural
Límite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Otol Neurotol
Asunto de la revista:
NEUROLOGIA
/
OTORRINOLARINGOLOGIA
Año:
2024
Tipo del documento:
Article