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A study on pulmonary functions in sports-players using machine learning and computational analysis

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Abstract

Machine learning (ML) can be used to predict the role of activities on the pulmonary functions of sportspersons and common people by developing a predictive model using relevant data. These pulmonary functions tests provide information on various respiratory parameters such as lung volumes, capacities, and flow rates, which can help to diagnose and monitor respiratory conditions. By measuring parameters such as maximal voluntary ventilation (MVV), forced vital capacity (FVC), and forced expiratory volume in 1 sec (FEV1), it is possible to evaluate the strength and endurance of the respiratory muscles and the ability of the lungs to exchange oxygen and carbon dioxide. Assessment of pulmonary function is particularly relevant for individuals engaging in sports or other physical activities, as it can help to identify potential respiratory limitations and optimize training and performance. First of all, the data is collected related to the activities of sportspersons and their corresponding pulmonary function measurements. This data includes information on the type of activity performed, the duration and intensity of the activity, and the corresponding pulmonary function measurements, such as forced vital capacity (FVC) and forced expiratory volume in one second (FEV1). The data pre-processing is performed to remove the redundant features and to handle the missing data. Feature engineering techniques are applied to extract useful features from the raw data that has helped to improve the performance of the proposed ML based predictive model. Then an appropriate ML algorithm that is well-suited to the data and the problem at hand has been adopted. A linear regression model, cascaded AdaBoost and Random Forest (RF) is used to predict the pulmonary function measurements based on the activity data of sportspersons. The model is then trained on a subset of the data. The model's performance is evaluated on a separate subset of the data to assess its accuracy and generalizability. Finally, the trained model is used to predict the pulmonary function measurements (PFMs) for new sportspersons based on their activity data. These predictions can help identify activities that may have a positive or negative impact on pulmonary function and can inform personalized training and activity plans for sportspersons.

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Authors and Affiliations

  1. Department of Physical Education and Research, Central South of University, Changsha, China

    Na Tan

  2. Sports and Art Institute, Hunan University of Chinese Medicine, Changsha, China

    Tuo Ren

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  1. Na Tan
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  2. Tuo Ren
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All the authors have equal contribution in this research study and in conceptualization, investigation, result-analysis and drafting of the manuscript.

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Correspondence to Tuo Ren.

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Tan, N., Ren, T. A study on pulmonary functions in sports-players using machine learning and computational analysis. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08477-2

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