Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 1, 2023
Date Accepted: Jan 16, 2024
Machine learning-based prediction of suicidality in adolescents with allergic rhinitis: Derivation and validation in two independent nationwide cohorts in South Korea
ABSTRACT
Background:
Given the additional risk of suicide-related behaviors that adolescents with allergic rhinitis (AR) have, it would be relevant to utilize the growing field of machine learning to better evaluate this risk.
Objective:
This study aimed to evaluate the validity and usefulness of a machine-learning (ML) model for predicting the suicide risks of patients with AR.
Methods:
We used data from two independent survey studies, Korea Youth Risk Behavior Web-based Survey (KYRBS; n=299,468) for the original dataset and the external validation Korea National Health and Nutrition Examination Survey (KNHANES; n=833), to predict suicide risks of AR in adolescent (age range 13 to 18) patients, noting 10,341 (3.45%) and 12 (1.44%) of patients with suicidal attempts in KYRBS and KNHANES respectively. The outcome of interest was suicide attempt risks. We selected various ML-based models with hyperparameter tuning in the discovery and performed an area under the receiver operating characteristic curve (AUROC) analysis in the train, test, and external-validation data.
Results:
The study dataset included 299,468 (KYRBS; original dataset) and 833 (KNHANES; external validation dataset) patients with AR recruited between 2005–2022. The best performing ML model was the random forest with a mean AUROC of 84.12% (95% CI, 83.98–84.27) in the original dataset. Applying this result to the external validation dataset revealed the best performance among the models, with an AUROC of 89.87% (sensitivity of 83.33%, specificity of 82.58%, accuracy of 82.59%, balanced accuracy of 82.96 %). While looking at feature importance, the five most important features in predicting suicide attempts in adolescent patients with AR are depression, stress status, academic achievement, age, and alcohol consumption.
Conclusions:
The study emphasizes the potential of ML models in predicting suicide risks in patients with AR, encouraging further application in other conditions to enhance adolescent health and decrease suicide rates.
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