DEEP LEARNING FOR ANALYSIS OF CHANGES IN VITAL CAPACITY AND BLOOD MARKERS AFTER SWIMMING MATCHES BASED ON BLENDED LEARNING

APRENDIZADO PROFUNDO PARA ANÁLISE DAS ALTERAÇÕES DA CAPACIDADE VITAL E MARCADORES SANGUÍNEOS APÓS JOGOS DE NATAÇÃO BASEADOS NO APRENDIZADO COMBINADO

APRENDIZAJE PROFUNDO PARA EL ANÁLISIS DE LOS CAMBIOS EN LA CAPACIDAD VITAL Y LOS MARCADORES SANGUÍNEOS DESPUÉS DE LOS PARTIDOS DE NATACIÓN BASADOS EN EL APRENDIZAJE COMBINADO

ABSTRACT

Introduction

Nowadays, more people are concerned with physical exercise and swimming competitions, as a major sporting event, have become a focus of attention. Such competitions require special attention to their athletes and the use of computational algorithms assists in this task.

Objective

To design and validate an algorithm to evaluate changes in vital capacity and blood markers of athletes after swimming matches based on combined learning.

Methods

The data integration algorithm was used to analyze changes in vital capacity and blood acid after combined learning swimming competition, followed by the construction of an information system model to calculate and process this algorithm.

Results

Comparative experiments show that the neural network algorithm can reduce the calculation time from the original initial time. In the latest tests carried out in about 10 seconds, this has greatly reduced the total calculation time.

Conclusion

According to the model requirements of the designed algorithm, practical help has been demonstrated by building a computational model. The algorithm can be optimized and selected according to the calculation model according to the reality of the application. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Deep Learning; Vital Capacity; Blood Chemical Analysis; Athletes