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To guarantee the safety and effectiveness of medical supplies like blood and vaccinations, careful environmental monitoring is necessary throughout transit. Even while real-time monitoring has advanced, current systems sometimes lack strong predictive ability to foresee unfavorable circumstances. The XGBoost-Ensemble for Medical Supplies Transport (XEMST), a unique stacking ensemble model created to predict interior humidity levels during travel, is presented in this paper to fill this gap. By utilizing XGBoost’s outstanding predictive fusion capabilities, the model incorporates predictions from fundamental machine learning methods, including Support Vector Machine, Random Forest, Decision Tree, and Linear Regression. XEMST outperformed individual models with a Root Mean Square Error (RMSE) of 2.22% and an R2 score of 0.96 when tested across 17 different transit situations. By enabling prompt responses, these predictive insights protect medical supply quality from environmental hazards. This study demonstrates how sophisticated ensemble learning frameworks have the potential to transform intelligent healthcare logistics.

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XEMST: Revolutionizing Smart Medical Logistics with Advanced Humidity Prediction Through Stacking Ensemble Models

  • 会议论文

  • 发表日期：

    2025年

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  • Tushar Vasudev,
  • Surbhi Ranga,
  • Sahil Sankhyan,
  • Praveen Kumar,
  • K. V. Uday,
  • Varun Dutt

摘要

To guarantee the safety and effectiveness of medical supplies like blood and vaccinations, careful environmental monitoring is necessary throughout transit. Even while real-time monitoring has advanced, current systems sometimes lack strong predictive ability to foresee unfavorable circumstances. The XGBoost-Ensemble for Medical Supplies Transport (XEMST), a unique stacking ensemble model created to predict interior humidity levels during travel, is presented in this paper to fill this gap. By utilizing XGBoost’s outstanding predictive fusion capabilities, the model incorporates predictions from fundamental machine learning methods, including Support Vector Machine, Random Forest, Decision Tree, and Linear Regression. XEMST outperformed individual models with a Root Mean Square Error (RMSE) of 2.22% and an R2 score of 0.96 when tested across 17 different transit situations. By enabling prompt responses, these predictive insights protect medical supply quality from environmental hazards. This study demonstrates how sophisticated ensemble learning frameworks have the potential to transform intelligent healthcare logistics.