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With rapid urbanization and population growth, efficient transportation systems are increasingly crucial, particularly in sectors like healthcare and pharmaceutical logistics, which face unique challenges. In Morocco, there is a lack of studies on pharmaceutical transport, especially regarding costs and delivery conditions, creating a need for a specialized model. Pharmaceutical transportation involves complex regression and classification tasks, further complicated by variable selection and correlated predictors. The Random Forest algorithm is a strong solution for these challenges, offering excellent predictive performance and variable selection. This paper introduces an innovative cost prediction model for pharmaceutical transport in Morocco, emphasizing the role of permutation importance in regression models and its impact on predictor correlations. The model achieves over 75% accuracy, demonstrating the effectiveness of Random Forests in addressing transportation complexities and providing valuable insights for Morocco’s pharmaceutical logistics sector.

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Urban Transport of Medical and Pharmaceutical Products: A Machine Learning Prediction Model Case Study

  • 会议论文

  • 发表日期：

    2025年

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  • Fadwa Farchi,
  • Chayma Farchi,
  • Badr Touzi,
  • Charif Mabrouki

摘要

With rapid urbanization and population growth, efficient transportation systems are increasingly crucial, particularly in sectors like healthcare and pharmaceutical logistics, which face unique challenges. In Morocco, there is a lack of studies on pharmaceutical transport, especially regarding costs and delivery conditions, creating a need for a specialized model. Pharmaceutical transportation involves complex regression and classification tasks, further complicated by variable selection and correlated predictors. The Random Forest algorithm is a strong solution for these challenges, offering excellent predictive performance and variable selection. This paper introduces an innovative cost prediction model for pharmaceutical transport in Morocco, emphasizing the role of permutation importance in regression models and its impact on predictor correlations. The model achieves over 75% accuracy, demonstrating the effectiveness of Random Forests in addressing transportation complexities and providing valuable insights for Morocco’s pharmaceutical logistics sector.