Optimizing Exercise Intensity and Duration with a Machine-Learning Model for Strengthening Immune System

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This study investigates the effects of exercise intensity and duration on the immune system response. 200 healthy individuals participated in the study, which was approved by the Research Ethics Committee of Allameh Tabataba’i University. Anthropometric indicators (weight, height, BMI) and maximum heart rate (HR) were measured. Each subject performed an individual exercise protocol on a treadmill according to the intensity Recommended by American College of Sports Medicine (ACSM). Blood samples were taken at baseline and after exercise to measure the levels of leukocytes. A random forest (RF) model was employed to predict the number of WBCs during the exercise. The model was evaluated based on quantitative metrics such as R, RMSE, MAE, RAE, RRSE and Nash-Sutcliffe efficiency coefficient (NSE).

The results of the study showed that the RF model can provide a reliable prediction of WBCs level within the healthy population. In addition to traditional variables such as baseline WBCs level, BMI and VO max, the RF model was verified to be robust for accurately predicting WBCs through exercise intensity and duration. Using the mean decrease in impurity (MDI) method, it was determined that intensity had a higher importance for predicting WBCs level.

Polar Electro OY, located in Kempele, Finland, is a leader in the research and development of advanced exercise technology solving the individual needs of exercisers. Enabling people to monitor their physiological parameters, Polar’s products have become essential tools for enhancing athletic performance.

Tanaka, is a Japanese exercise physiologist and professor of physical education at Kyoto University and the Vice Minister of Education, Culture, Sports, Science and Technology. He has done extensive research on the physiological effects of exercise, profiling exercise intensity, energy expenditure and physical fitness.

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