Applying artificial intelligence techniques for diagnosing diseases in hospitals often provides advanced medical services to patients such as the diagnosis of leukemia. On the other hand, surgery and bone marrow sampling, especially in the diagnosis of childhood leukemia, are even more complex and difficult, resulting in increased human error and procedure time decreased patient satisfaction and increased costs. This study investigates the use of neuro-fuzzy and group method of data handling, for the diagnosis of acute leukemia in children based on the complete blood count test. Furthermore, a principal component analysis is applied to increase the accuracy of the diagnosis. The results show that distinguishing between patient and non-patient individuals can easily be done with adaptive neuro-fuzzy inference system, whereas for classifying between the types of diseases themselves, more pre-processing operations such as reduction of features may be needed. The proposed approach may help to distinguish between two types of leukemia including acute lymphoblastic leukemia and acute myeloid leukemia. Based on the sensitivity of the diagnosis, experts can use the proposed algorithm to help identify the disease earlier and lessen the cost.

在医院应用人工智能技术诊断疾病，往往为患者提供先进的医疗服务，例如白血病的诊断。另一方面，手术和骨髓取样，特别是在儿童白血病的诊断中，更加复杂和困难，导致人为错误增加，手术时间降低，患者满意度下降，成本增加。本研究探讨了使用神经模糊和分组数据处理方法，基于全血细胞计数测试诊断儿童急性白血病。此外，应用主成分分析来提高诊断的准确性。结果表明，使用自适应神经模糊推理系统可以轻松区分患者和非患者个体，而对于疾病类型本身的分类，可能需要更多的预处理操作，例如减少特征。所提出的方法可能有助于区分两种类型的白血病，包括急性淋巴细胞白血病和急性髓细胞白血病。根据诊断的敏感性，专家可以使用所提出的算法来帮助更早地识别疾病并降低成本。