Department of Hematology, Yancheng First Hospital Affiliated Hospital of Nanjing University Medical School, Yancheng No. 1 Peoples' Hospital, Yancheng 224006, China.
Department of Hematology, Xuzhou Medical University, Xuzhou 221004, China.
Comput Math Methods Med. 2022 Jun 28;2022:9620780. doi: 10.1155/2022/9620780. eCollection 2022.
The prediction model of bone marrow infiltration (BMI) in patients with malignant lymphoma (ML) was established based on the logistic regression and the XGBoost algorithm. The model's prediction efficiency was evaluated.
A total of 120 patients diagnosed with ML in the department of hematology from January 2018 to January 2021 were retrospectively selected. The training set ( = 84) and test set ( = 36) were randomly divided into 7 : 3, and logistic regression and XGBoost algorithm models were constructed using the training set data. Predictors of BMI were screened based on laboratory indicators, and the model's efficacy was evaluated using test set data.
The prediction algorithm model's top three essential characteristics are the blood platelet count, soluble interleukin-2 receptor, and non-Hodgkin's lymphoma. The area under the curve of the logistic regression model for predicting the BMI of patients with ML was 0.843 (95% CI: 0.7610.926). The area under the curve of the XGBoost model is 0.844 (95% CI: 0.7650.937).
The prediction model constructed in this study based on logistic regression and XGBoost algorithm has a good prediction model. The results showed that blood platelet count and soluble interleukin-2 receptor were good predictors of BMI in ML patients.
基于逻辑回归和 XGBoost 算法建立恶性淋巴瘤（ML）患者骨髓浸润（BMI）的预测模型，并评估模型的预测效能。
回顾性选取 2018 年 1 月至 2021 年 1 月我院血液科收治的 120 例 ML 患者，采用随机数字表法将患者分为训练集（n=84）和测试集（n=36），比例为 7:3。利用训练集数据构建逻辑回归和 XGBoost 算法模型，筛选 BMI 的预测指标，采用测试集数据评估模型的效能。
预测算法模型的前三个重要特征是血小板计数、可溶性白细胞介素-2 受体和非霍奇金淋巴瘤。逻辑回归模型预测 ML 患者 BMI 的曲线下面积为 0.843（95%CI：0.7610.926），XGBoost 模型的曲线下面积为 0.844（95%CI：0.7650.937）。
本研究构建的基于逻辑回归和 XGBoost 算法的预测模型具有良好的预测效果。结果表明，血小板计数和可溶性白细胞介素-2 受体是 ML 患者 BMI 的良好预测指标。
