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<p>We developed a comprehensive system architecture that incorporates a predictive module for assessing hospitalization risk due to asthma exacerbations. This module is specifically designed to promote equitable access to healthcare services for underserved populations, particularly those residing in rural and socio-economically disadvantaged areas. The predictive model, based on LightGBM, demonstrated superior performance, achieving high evaluation metrics, particularly in settings with synthetic data, and maintained a minimal false negative rate, which is a critical requirement in clinical decision-making. The proposed architecture further integrates a synthetic data generator to establish a dual-layered security mechanism in conjunction with federated learning, thereby reinforcing data privacy and protection against inversion attacks. Additionally, we proposed a personalized aggregation strategy, tailored to prioritize model updates from clients with lower false negative rates. This approach aims to enhance the global model’s predictive reliability, ensuring both quality of care and equitable access to accurate and timely medical interventions for underserved populations.</p>

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Predicting asthma-related hospitalizations in underserved and rural populations in Algeria

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Amin Bouamar,
  • Ghalem Belalem,
  • Boumedyen Belaid,
  • Dalila Benatta,
  • Abdelmadjid Snouber

摘要

We developed a comprehensive system architecture that incorporates a predictive module for assessing hospitalization risk due to asthma exacerbations. This module is specifically designed to promote equitable access to healthcare services for underserved populations, particularly those residing in rural and socio-economically disadvantaged areas. The predictive model, based on LightGBM, demonstrated superior performance, achieving high evaluation metrics, particularly in settings with synthetic data, and maintained a minimal false negative rate, which is a critical requirement in clinical decision-making. The proposed architecture further integrates a synthetic data generator to establish a dual-layered security mechanism in conjunction with federated learning, thereby reinforcing data privacy and protection against inversion attacks. Additionally, we proposed a personalized aggregation strategy, tailored to prioritize model updates from clients with lower false negative rates. This approach aims to enhance the global model’s predictive reliability, ensuring both quality of care and equitable access to accurate and timely medical interventions for underserved populations.