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Development and Validation of an Explainable Machine Learning Model for Identifying Hypertension Status in Patients

Feng Wei1, Tianyu Wu1, Jinggang Deng1

  • 1Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.

Journal of Clinical Hypertension (Greenwich, Conn.)
|April 7, 2026
PubMed
Summary
This summary is machine-generated.

Obstructive sleep apnea (OSA) increases hypertension risk. An interpretable machine learning model accurately identifies hypertension in OSA patients, using factors like oxygen levels and BMI, aiding clinical diagnosis.

Keywords:
hypertensionmachine learningnocturnal hypoxiaobstructive sleep apneaprecision medicine

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Area of Science:

  • Sleep Medicine
  • Cardiology
  • Artificial Intelligence

Background:

  • Obstructive sleep apnea (OSA) is a significant global risk factor for hypertension (HTN).
  • OSA-related HTN is often underdiagnosed due to limitations in conventional blood pressure monitoring.
  • Accurate identification of HTN in OSA patients is crucial for effective management.

Purpose of the Study:

  • To develop an interpretable machine learning (ML) model for identifying hypertension status in patients with obstructive sleep apnea.
  • To identify key predictors of hypertension within the OSA patient population.
  • To create a tool for facilitating clinical application and precise sleep medicine.

Main Methods:

  • Analysis of data from 1771 diagnosed OSA patients, split into training (70%) and testing (30%) sets.
  • Utilized LASSO regression and the Boruta algorithm to identify significant predictors.
  • Evaluated 14 ML models, with the adaptive boosting model showing superior performance (AUC 0.830).
  • Employed SHAP analysis for model interpretability and to reveal feature-specific relationships.

Main Results:

  • Identified seven key predictors: age, BMI, neck circumference, lowest oxygen saturation, % time with SpO2 < 90%, AHI, and TG index.
  • The adaptive boosting model achieved an AUC of 0.830 on the test set.
  • SHAP analysis confirmed model logic and demonstrated dose-response relationships, emphasizing nocturnal hypoxia, metabolic, and anatomical factors.

Conclusions:

  • An interpretable ML model can effectively identify HTN status in OSA patients.
  • Nocturnal hypoxia burden is a stronger predictor of HTN in OSA than respiratory event frequency alone.
  • The developed prediction tool offers a valuable resource for precise sleep medicine and clinical practice.