Preventing chronic disease
Interpretable Machine Learning for Predicting Early Onset Hypertension in the US
Par Wu LW, Kim K, Washington P et 1 autres.
Contenu en anglais
Résumé
Introduction
Early onset hypertension is often overlooked despite its association with lifelong cardiovascular disease risk. We developed an interpretable machine learning model to predict early onset hypertension in US adults using the 2022 Medical Expenditure Panel Survey, incorporating demographic, socioeconomic, health care utilization, and comorbidity features.
Methods
We analyzed data on adults aged 55 years or younger. Hypertension was defined by International Classification of Diseases, 10th Revision, Clinical Modification code number I10 or self-reported physician diagnosis at or before age 55 years. Eighty-four variables were screened; Least Absolute Shrinkage and Selection Operator reduced the set to 45 variables. Extreme gradient boosting was trained with stratified 5-fold cross-validation. Performance metrics included accuracy, precision, recall, F2 score, area under the receiver operating characteristic curve (ROC AUC), and area under the precision-recall curve (PR AUC). We assessed fairness across subgroups and used Shapley Additive Explanations to quantify feature influence.