Department of Exercise Physiology, Faculty of Physical Education and Sport Sciences, Allameh Tabataba'i University, Tehran, Iran.
Department of Exercise Physiology, Faculty of Physical Education and Sports Sciences, Allameh Tabataba'i University, Tehran, Iran.
Sci Rep. 2023 May 22;13(1):8207. doi: 10.1038/s41598-023-34974-3.
One of the important concerns in the field of exercise immunology is determining the appropriate intensity and duration of exercise to prevent suppression of the immune system. Adopting a reliable approach to predict the number of white blood cells (WBCs) during exercise can help to identify the appropriate intensity and duration. Therefore, this study was designed to predict leukocyte levels during exercise with the application of a machine-learning model. We used a random forest (RF) model to predict the number of lymphocytes (LYMPH), neutrophils (NEU), monocytes (MON), eosinophils, basophils, and WBC. Intensity and duration of exercise, WBCs values before exercise training, body mass index (BMI), and maximal aerobic capacity (VO max) were used as inputs and WBCs values after exercise training were assessed as outputs of the RF model. In this study, the data was collected from 200 eligible people and K-fold cross-validation was used to train and test the model. Finally, model efficiency was assessed using standard statistics (root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative square error (RRSE), coefficient of determination (R), and Nash-Sutcliffe efficiency coefficient (NSE)). Our findings revealed that the RF model performed well for predicting the number of WBC with RMSE = 0.94, MAE = 0.76, RAE = 48.54, RRSE = 48.17, NSE = 0.76, and R = 0.77. Furthermore, the results showed that intensity and duration of exercise are more effective parameters than BMI and VO max to predict the number of LYMPH, NEU, MON, and WBC during exercise. Totally, this study developed a novel approach based on the RF model using the relevant and accessible variables to predict WBCs during exercise. The proposed method can be applied as a promising and cost-effective tool for determining the correct intensity and duration of exercise in healthy people according to the body's immune system response.
运动免疫学领域的一个重要关注点是确定适当的运动强度和持续时间，以防止免疫系统受到抑制。采用可靠的方法来预测运动过程中的白细胞（WBC）数量，可以帮助确定适当的运动强度和持续时间。因此，本研究旨在应用机器学习模型来预测运动过程中的白细胞水平。我们使用随机森林（RF）模型来预测淋巴细胞（LYMPH）、中性粒细胞（NEU）、单核细胞（MON）、嗜酸性粒细胞、嗜碱性粒细胞和 WBC 的数量。运动强度和持续时间、运动训练前的 WBC 值、体重指数（BMI）和最大有氧能力（VO max）被用作 RF 模型的输入，运动训练后的 WBC 值被评估为 RF 模型的输出。在这项研究中，数据是从 200 名符合条件的人那里收集的，使用 K 折交叉验证来训练和测试模型。最后，使用标准统计数据（均方根误差（RMSE）、平均绝对误差（MAE）、相对绝对误差（RAE）、根相对平方误差（RRSE）、决定系数（R）和纳什-苏特克里夫效率系数（NSE））评估模型效率。我们的研究结果表明，RF 模型在预测 WBC 数量方面表现良好，RMSE=0.94，MAE=0.76，RAE=48.54，RRSE=48.17，NSE=0.76，R=0.77。此外，结果表明，与 BMI 和 VO max 相比，运动强度和持续时间是预测运动过程中 LYMPH、NEU、MON 和 WBC 数量的更有效参数。总的来说，本研究基于 RF 模型开发了一种新方法，使用相关且可访问的变量来预测运动过程中的 WBC。该方法可以作为一种有前途且具有成本效益的工具，根据人体免疫系统的反应，确定健康人群运动的正确强度和持续时间。
