African Center of Excellence in Data Science (ACE-DS), College of Business and Economics, University of Rwanda, Kigali, Rwanda.
Human Resource for Health Secretariat, Ministry of Health, Kigali, Rwanda.
Pan Afr Med J. 2022 Jun 2;42:89. doi: 10.11604/pamj.2022.42.89.33833. eCollection 2022.
recent initiatives in healthcare reform have pushed for a better understanding of data complexity and revolution. Given the global prevalence of Non-Communicable Diseases (NCD) and the economic and clinical burden they impose, it is recommended that the management of essential medicines used to treat them be renovated and optimized through the application of predictive modeling such a RF model.
in this study, a series of data pre-processing activities were used to select the top seventeen (17) NCD essential medicines most commonly used for treating common and frequent NCD. The study focused on machine learning (ML) applications, whereby a random forest (RF) model was applied to predict the demand using essential medicines consumption data from 2015 to 2019 for approximately 500 medical products.
with a seventy-eight (78) percent accuracy rate for the training set and a 71 percent accuracy rate for the testing set, the RF model predicted the trend in demand for 17 NCD essential medicines. This was achieved by entering the month, year, district, and name of the NCD essential medicine. Based on historical consumption data, the RF model can thus be used to predict demand trends. Our findings showed that the RF model is talented to commendably perform as a predicting model.
the study concluded that RF has the ability to optimize health supply chain planning and operational management by boosting the accuracy in predicting the demand trend for NCD essential medicines.
最近的医疗改革举措推动了对数据复杂性和变革的更好理解。鉴于全球非传染性疾病（NCD）的流行以及它们带来的经济和临床负担，建议通过应用预测建模（如 RF 模型）来改进和优化用于治疗这些疾病的基本药物的管理。
在这项研究中，进行了一系列数据预处理活动，以选择最常用于治疗常见和频繁 NCD 的十七（17）种 NCD 基本药物。该研究侧重于机器学习（ML）应用，应用随机森林（RF）模型来预测需求，使用 2015 年至 2019 年约 500 种医疗产品的基本药物消费数据。
RF 模型对训练集的准确率为 78%，对测试集的准确率为 71%，预测了 17 种 NCD 基本药物的需求趋势。通过输入月份、年份、地区和 NCD 基本药物名称来实现这一目标。基于历史消费数据，因此可以使用 RF 模型来预测需求趋势。我们的研究结果表明，RF 模型能够出色地执行预测模型。
研究得出结论，RF 具有通过提高 NCD 基本药物需求趋势预测的准确性来优化卫生供应链规划和运营管理的能力。
