Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.

中文翻译：

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控制高血压和1型糖尿病患者心血管疾病风险的综合方法

如今，空气污染，吸烟，使用脂肪酸和现成食品等已加剧了心脏病。因此，控制此类疾病的风险可以预防或降低其发病率。本研究旨在开发一种综合方法，包括马尔可夫决策过程（MDP）和遗传算法（GA），以控制高血压和1型糖尿病患者的心血管疾病风险。首先，针对灰狼优化算法对遗传算法的效率进行了评估，然后揭示了遗传算法的优越性。接下来，使用MDP来估计心血管疾病的风险。为此，模型输入首先使用由GA在伊朗德黑兰医科大学开发的经过验证的微仿真模型确定，用于筛查心血管疾病。然后，相应地定义模型输入因子，并使用这些输入确定三个风险估计模型。这些模型的结果支持WHO的指南，该指南为具有较高预期LYs的患者提供了很高的折扣。要开发MDP方法，尽管风险模型与实际风险之间存在差异，但仍应采用行之有效的政策。最后，进行敏感性分析以研究总药物成本对参数变化的影响。尽管风险模型和实际风险之间存在差异，但仍应采用行之有效的政策。最后，进行敏感性分析以研究总药物成本对参数变化的影响。尽管风险模型和实际风险之间存在差异，但仍应采用行之有效的政策。最后，进行敏感性分析以研究总药物成本对参数变化的影响。