Development and Validation of a Machine Learning Model for Detection and Classification of Vertigo

Abstract

Purpose: This study aims to investigate whether artificial intelligence can improve the diagnostic accuracy of vertigo related diseases.

Experimental design: Based on the clinical guidelines, clinical symptoms and laboratory test results were extracted from electronic medical records as variables. These variables were then input into a machine learning diagnostic model for classification and diagnosis. This study encompasses two primary objectives: Task 1 to distinguish between patients with Benign Paroxysmal Positional Vertigo (BPPV) and non-BPPV. In Task 2, further classifying non-BPPV patients into Ménière's Disease (MD), Vestibular Migraine (VM), and Sudden Sensorineural Hearing Loss accompanied by Vertigo (SSNHLV). The sensitivity, precision, and area under the curve (AUC) metric is primarily used to assess the performance of the machine learning model development phase in a prospective validation cohort.

Results: In our study, 1789 patients were recruited as the training cohort and 1148 patients as the prospective validation cohort. The comprehensive diagnostic performance of the XGBoost model surpasses that of traditional models. The sensitivity, accuracy, and AUC in task 1 were 98.32%, 87.03%, and 0.947, respectively. In task 2, the sensitivity values for MD, SSNHLV, and VM were 89.00%, 100.0%, and 79.40%, respectively. The precision values were 88.80%, 100.0%, and 80.00%, respectively. The AUC values were 0.933, 1.000, and 0.931, respectively. The model can significantly improve the accuracy of diagnosing vertigo diseases.

Conclusions: This system may enhance the accuracy of classification and diagnosis of vertigo diseases. It offers initial therapy or referrals to clinical doctors, particularly in resource-limited settings.

Level of evidence: N/A Laryngoscope, 2024.

Keywords: XGBoost; diagnosis; electronic health record; peripheral vertigo diseases.