THE IMPROVEMENT OF PCA ALGORITHM AND ITS APPLICATION IN THE PREDICTION OF ELBOW KNEE JOINT INJURY

Abstract

ABSTRACT Objective: There were many constraints produced by training time and joint injury to analyze the influence of the training intensity on the elbow and knee joints of athletes during the training process. Methods: An improved algorithm-based master component analysis (PCA) modeling method is proposed .1 4 4 athletes were selected in xxx and compared in three groups. Results: The improved PCA models for injury prediction were applied to athletes from group A, the traditional injury models for prediction were adopted for athletes from group B, and athletes from group C received the hospital physical examinations. The results showed that the accuracy of elbow injury in group A due to excessive exercise was 66.86%, the accuracy of hospital physical examination in group C was 67%, and the accuracy of the traditional algorithm in group B was 50%, finding that the accuracy of group A was obviously different from group B (P < 0.05). Compared with other injuries caused by excessive friction, the detection accuracy of knee injuries caused by excessive friction in group A was 62%, that in group B was 44%, and that in group C was 63%. There was a statistically marked difference between groups A and B (P < 0.05). Conclusions: A PCA - based model of athletes’ overtraining injury has high accuracy and adaptability, predicting elbow injury. Level of evidence II; Therapeutic studies - investigation of treatment results.