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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases.

Ioannis Bilionis^1,2, Ricardo C Berrios¹, Luis Fernandez-Luque¹

¹Adhera Health, Santa Cruz, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|June 12, 2025

Summary

Machine learning (ML) models show sex and age biases in chronic disease prediction. Addressing model arbitrariness, not just data representation, is crucial for equitable clinical decision-making.

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Area of Science:

Biomedical Informatics
Artificial Intelligence in Healthcare
Health Equity

Background:

Machine learning (ML) algorithms are increasingly used for clinical decision-making.
Underrepresentation of demographic groups in training data can lead to performance disparities.
Existing research highlights potential inequities in ML models for chronic diseases.

Purpose of the Study:

To investigate sex- and age-related inequities in chronic disease datasets and ML models.
To introduce a novel analytical framework to assess model arbitrariness beyond traditional metrics.
To evaluate the impact of data representativeness and model arbitrariness on predictive accuracy.

Main Methods:

Analysis of chronic disease data from over 25,000 individuals.
Application of a novel framework combining systematic arbitrariness with accuracy and data complexity metrics.
Evaluation of ML model performance across different demographic groups (sex and age).

Main Results:

Mild sex-related disparities observed, with higher predictive accuracy for males.
Significant age-related differences found, favoring younger patients.
Older patients exhibited inconsistent predictive accuracy, correlated with higher data complexity and lower model performance.

Conclusions:

Data representativeness alone does not ensure equitable ML outcomes in healthcare.
Model arbitrariness is a significant factor contributing to performance disparities.
Addressing model arbitrariness is essential before clinical deployment of ML tools.