This study proposes the concept of Analytic Hierarchy Processing (AHP) on the market of active substances used in treatment of HIV and checks the control factors and criteria interconnection and implements Random Forest forecasting model. The new method must help to improve the management decision-making process in the fields of healthcare government budget planning. It has become a prime concern for understanding and comparing of publicly available information with internal market data and the consequences of companies` and government`s actions in choosing the best approach for correct construction of to reduce HIV incidence in Russia. The paper develops the forecasting model of one of the parameters, which has a substantial role in decision-making process. The medication market data in this study represents the cumulative daily concluded contracts, used in treatment of HIV in Russia, the level of HIV incidence (yearly) and federal budget on healthcare (yearly). The proposed approach have more than 82% average accuracy at predicting the sum of medication contract prices at the 3-year time period. The received figures are effective in predicting the factors` behavior in future. It can be used for improved modulation of AHP and consequently, the overall accuracy of the model structure.

中文翻译：

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借助层次分析法进行药物市场绩效分析

这项研究提出了用于治疗HIV的活性物质市场上的层次分析法（AHP）的概念，并检查了控制因素和标准之间的相互联系，并实施了随机森林预测模型。这种新方法必须有助于改善医疗保健政府预算计划领域的管理决策过程。在理解和比较可公开获取的信息与内部市场数据以及公司和政府采取的行动以选择正确的方法来减少俄罗斯的艾滋病毒感染率的最佳方法的后果方面，这已经成为首要关注的问题。本文开发了其中一种参数的预测模型，该模型在决策过程中具有重要作用。这项研究中的药品市场数据代表了俄罗斯每日用于治疗艾滋病毒的每日累计合同数，艾滋病毒感染水平（每年）和联邦医疗保健预算（每年）。所提出的方法在预测3年时间段内的药物合同价格总和时，平均准确性超过82％。收到的数字可以有效地预测将来的因素行为。它可以用于改进AHP的调制，从而提高模型结构的整体准确性。收到的数字可以有效地预测将来的因素行为。它可以用于改进AHP的调制，从而提高模型结构的整体精度。收到的数字可以有效地预测将来的因素行为。它可以用于改进AHP的调制，从而提高模型结构的整体精度。