Machine Learning Technique to Analyze the Health Condition of Athletes and Predict their Performance

In the past few years, the application of machine learning techniques in the field of sports science has gained considerable attention. The ability to analyse vast amounts of data and extract meaningful insights has opened up new opportunities for understanding the health conditions of athletes and predicting their performance. In this paper, we explore the use of machine learning techniques for analysing the health conditions of athletes and predicting their performance. Here, we use the athletes performance dataset to predict the performance class of athletes. The data is preprocessed and transformed into features that are relevant to the health and performance of athletes. The model is trained on the dataset of previous performance records and health data of athletes to make predictions for new athletes. In this proposed framework, we apply various machine learning algorithms such as random forest, logistic regression, gradient boosting, decision tree classifier and K-neighbors classifier, to the data set. We examine the advantages and limitations of each technique and provide examples of how they can be used to analyse athlete health and performance. Also, we compare the accuracy of each classifier and select the one that has the highest accuracy on the test set. In the experimental results, random forest classifier outperformed other algorithms in predicting the performance class. The outcome demonstrate the efficacy of the proposed technique for predicting the performance of athletes with high accuracy, which can be used to support coaches and trainers in optimising training plans and preventing injuries.