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Development and Validation of an Interpretable Machine Learning Model Based on Routine Blood Biomarkers: For

Dan He1, Yiting Liu1, Jing Ke2

  • 1Chongqing Medical University, Chongqing 400016, China.

Diagnostics (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study developed an interpretable machine learning model using routine blood tests to predict age-related hearing loss (ARHL) risk. The model shows strong performance, offering a new tool for early ARHL detection in large populations.

Keywords:
age-related hearing lossblood biomarkersinterpretabilitymachine learningpredictive model

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Data Science

Background:

  • Age-related hearing loss (ARHL) is a prevalent sensory impairment in older adults.
  • Early prediction and intervention are vital for enhancing quality of life in the elderly.
  • Routine blood biomarkers offer a potential avenue for non-invasive ARHL risk assessment.

Purpose of the Study:

  • To develop and validate an interpretable machine learning model for predicting ARHL risk using routine blood biomarkers.
  • To identify key blood markers associated with ARHL.
  • To create an accessible tool for real-time clinical risk assessment.

Main Methods:

  • Utilized data from 542 participants (271 ARHL, 271 controls) from the NHANES database.
  • Developed and optimized a predictive model (glmBoost+Stepglm[forward]) through extensive algorithm comparison.
  • Validated the model internally and externally, employing SHAP for feature interpretation and R Shiny for a web-based tool.

Main Results:

  • The model achieved high performance, with AUCs of 0.948 (training) and 0.893-0.945 (internal validation).
  • External validation showed good performance (AUC 0.839), with 77.2% accuracy.
  • Key predictive features identified include glycated hemoglobin (HbA1c), mean corpuscular volume (MCV), and blood glucose.

Conclusions:

  • An interpretable machine learning model using routine blood biomarkers for ARHL risk stratification was successfully developed and validated.
  • The model demonstrates robust generalization capabilities and provides insights into ARHL pathogenesis.
  • An interactive web tool facilitates real-time risk assessment, serving as a valuable prescreening tool for large populations.