The purpose of the present study is to analyze the prognostic factors of acute leukemia and to construct a decision model based on a causal relationship between the factors of this disease to assist medical specialists. In medical decisions, to reach effective, quick, and reliable results, there is a need for a simple decision-making model based on a specialist’s self-assessment. It may help the medical team before final diagnosis by costly and time-consuming procedures such as bone marrow sampling and pathological test as well as provide an appropriate prognosis and diagnosis tool. Because of the complex and not the well-defined structure of medical data, the use of intelligent methods must be considered. For this purpose, first, a data-driven Bayesian network (BN) and Greedy algorithm are employed to determine causal relationships and probability between nodes using the real set of data. Then, these causal relationships will form based on the fuzzy cognitive map (FCM). Finally, according to scenarios defined, the results are analyzed. These analyses are also repeated for each type of acute leukemia including acute lymphocytic leukemia (ALL) and acute myelocytic leukemia (AML).

本研究的目的是分析急性白血病的预后因素，并基于该疾病因素之间的因果关系构建决策模型以协助医学专家。在医疗决策中，为了获得有效、快速和可靠的结果，需要一个基于专家自我评估的简单决策模型。它可以通过昂贵且耗时的程序（例如骨髓取样和病理检查）在最终诊断之前帮助医疗团队，并提供适当的预后和诊断工具。由于医疗数据的结构复杂且没有明确定义，因此必须考虑使用智能方法。为此，首先，使用数据驱动的贝叶斯网络 (BN) 和贪婪算法来确定使用真实数据集的节点之间的因果关系和概率。然后，这些因果关系将基于模糊认知图（FCM）形成。最后，根据定义的场景，对结果进行分析。还对每种类型的急性白血病重复这些分析，包括急性淋巴细胞白血病 (ALL) 和急性髓细胞白血病 (AML)。