Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model
Abstract 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 identif...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34974-3 |
| _version_ | 1827939930569965568 |
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| author | Shirin Asadi Bakhtyar Tartibian Mohammad Ali Moni |
| author_facet | Shirin Asadi Bakhtyar Tartibian Mohammad Ali Moni |
| author_sort | Shirin Asadi |
| collection | DOAJ |
| description | Abstract 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 (VO2 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 (R2), 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 R2 = 0.77. Furthermore, the results showed that intensity and duration of exercise are more effective parameters than BMI and VO2 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. |
| first_indexed | 2024-03-13T09:03:01Z |
| format | Article |
| id | doaj.art-bf00d31f48424de19bcccf2b24004bb0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T09:03:01Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-bf00d31f48424de19bcccf2b24004bb02023-05-28T11:16:02ZengNature PortfolioScientific Reports2045-23222023-05-0113111010.1038/s41598-023-34974-3Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning modelShirin Asadi0Bakhtyar Tartibian1Mohammad Ali Moni2Department of Exercise Physiology, Faculty of Physical Education and Sport Sciences, Allameh Tabataba’i UniversityDepartment of Exercise Physiology, Faculty of Physical Education and Sports Sciences, Allameh Tabataba’i UniversityArtificial Intelligence and Data Science, Faculty of Health and Behavioural Sciences, School of Health and Rehabilitation Sciences, The University of QueenslandAbstract 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 (VO2 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 (R2), 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 R2 = 0.77. Furthermore, the results showed that intensity and duration of exercise are more effective parameters than BMI and VO2 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.https://doi.org/10.1038/s41598-023-34974-3 |
| spellingShingle | Shirin Asadi Bakhtyar Tartibian Mohammad Ali Moni Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model Scientific Reports |
| title | Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model |
| title_full | Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model |
| title_fullStr | Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model |
| title_full_unstemmed | Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model |
| title_short | Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model |
| title_sort | determination of optimum intensity and duration of exercise based on the immune system response using a machine learning model |
| url | https://doi.org/10.1038/s41598-023-34974-3 |
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