Artificial Intelligence Reveals the Predictions of Hematological Indexes in Children with Acute Leukemia

Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute leukemia. This study aimed to develop an early and comprehensive predictor for hematologic malignancies in children by examining nutritional markers, key leukemia indicators, and granulocytes in patients' blood. Using a machine learning algorithm and ten indices, 826 pediatric patients with ALL and 255 children with AML were analyzed, comparing them with a control group of 200 healthy children. The study revealed notable differences, including higher indicators in boys compared to girls and significant variations in most biochemical indicators between leukemia patients and healthy children. Employing a random forest model resulted in an Area Under the Curve (AUC) of 0.950 for predicting leukemia subtypes and an AUC of 0.909 for forecasting AML. This research introduces an efficient diagnostic tool for early screening of childhood blood cancers and underscores the potential of artificial intelligence in modern healthcare.

儿童白血病作为一种常见的儿科癌症，急性淋巴细胞白血病（ALL）和急性髓细胞白血病（AML）为其主要表现形式。及时的治疗显著提高了急性白血病患者存活率。本研究旨在通过检验营养标志物、关键白血病指标和患者血液中的粒细胞，开发一种针对儿童血液恶性肿瘤的早期和全面预测模型。利用机器学习算法和十个指标，对826例急性淋巴细胞白血病患儿和255例急性髓细胞白血病儿童进行了分析，并将其与200名健康儿童的对照组进行比较。研究揭示了显著的差异，包括男童相较于女童具有更高的指标，以及白血病患者的多数生化指标与健康儿童之间存在显著差异。采用随机森林模型，预测白血病亚型的曲线下面积（AUC）达到0.950，预测AML的AUC达到0.909。本研究提出了一种高效的诊断工具，用于儿童血液癌症的早期筛查，并强调了人工智能在现代医疗保健中的潜力。