Diagnosis of Acute Central Dizziness With Simple Clinical Information Using Machine Learning
- Background: Acute dizziness is a common symptom among patients visiting
emergency medical centers. Extensive neurological examinations aimed at delineating
the cause of dizziness often require experience and specialized training. We tried to
diagnose central dizziness by machine learning using only basic clinical information.
Methods: Patients were enrolled who had visited an emergency medical center with
acute dizziness and underwent diffusion-weighted imaging. The enrolled patients were
dichotomized as either having central (with a corresponding central lesion) or non-central
dizziness. We obtained patient demographics, risk factors, vital signs, and presentation
(non-whirling type dizziness or vertigo). Various machine learning algorithms were
used to predict central dizziness. The area under the receiver operating characteristic
curve (AUROC) was measured to evaluate diagnostic accuracy. The SHapley Additive
exPlanations (SHAP) value was used to explain the importance of each factor.
Results: Of the 4,481 visits, 414 (9.2%) were determined as central dizziness. Central
dizziness patients were more often older and male and had more risk factors and higher
systolic blood pressure. They also presented more frequently with non-whirling type
dizziness (79 vs. 54.4%) than non-central dizziness. Catboost model showed the highest
AUROC (0.738) with a 94.4% sensitivity and 31.9% specificity in the test set (n = 1,317).
The SHAP value was highest for previous stroke presence (mean; 0.74), followed by male
(0.33), presentation as non-whirling type dizziness (0.30), and age (0.25).
Conclusions: Machine learning is feasible for classifying central dizziness using
demographics, risk factors, vital signs, and clinical dizziness presentation, which are
obtainable at the triage.
- 강동화; 권순억; 김범준; 김용환; 김종성; 이은재; 장수경; 장준영
- Issued Date
- dizziness; machine learning; Neurology; stroke; vertebrobasilar insufficiency; vertigo
- Frontiers in Neurology
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