Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study.
Biomed Eng Online
; 22(1): 45, 2023 May 13.
Article
em En
| MEDLINE
| ID: mdl-37179307
ABSTRACT
PURPOSE:
This study aimed to develop an interpretable machine learning model to predict the onset of myopia based on individual daily information.METHOD:
This study was a prospective cohort study. At baseline, non-myopia children aged 6-13 years old were recruited, and individual data were collected through interviewing students and parents. One year after baseline, the incidence of myopia was evaluated based on visual acuity test and cycloplegic refraction measurement. Five algorithms, Random Forest, Support Vector Machines, Gradient Boosting Decision Tree, CatBoost and Logistic Regression were utilized to develop different models and their performance was validated by area under curve (AUC). Shapley Additive exPlanations was applied to interpret the model output on the individual and global level.RESULT:
Of 2221 children, 260 (11.7%) developed myopia in 1 year. In univariable analysis, 26 features were associated with the myopia incidence. Catboost algorithm had the highest AUC of 0.951 in the model validation. The top 3 features for predicting myopia were parental myopia, grade and frequency of eye fatigue. A compact model using only 10 features was validated with an AUC of 0.891.CONCLUSION:
The daily information contributed reliable predictors for childhood's myopia onset. The interpretable Catboost model presented the best prediction performance. Oversampling technology greatly improved model performance. This model could be a tool in myopia preventing and intervention that can help identify children who are at risk of myopia, and provide personalized prevention strategies based on contributions of risk factors to the individual prediction result.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Refração Ocular
/
Miopia
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adolescent
/
Child
/
Humans
Idioma:
En
Revista:
Biomed Eng Online
Assunto da revista:
ENGENHARIA BIOMEDICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China