STUDY ON STUDENTS' PHYSICAL HEALTH ASSESSMENT METHOD BASED ON IMPROVED PRINCIPAL COMPONENT ANALYSIS

Abstract

This study aims to develop an improved principal component analysis (PCA) based physical health assessment method for civil aviation flight students. By introducing sparsity constraints and kernel techniques, the interpretation ability of PCA and the ability to deal with nonlinear relations are enhanced, so as to more accurately and efficiently assess the physical health status of flight students. Firstly, this paper preprocesses the health data of flight students, including the standardization of the data, to eliminate the dimensional influence between different indicators. Then, sparse PCA (SPCA) method is introduced to improve the interpretability of the model by adding sparsity constraints, making important features more prominent while reducing the data dimension. In addition, considering the complex nonlinear structures that may exist in health data, this study uses kernel PCA (KPCA) technology to select appropriate kernel functions to map the data to a high-dimensional space to reveal the deep structure and correlation of the data. The improved PCA method combining sparsity and kernel technique not only improves the accuracy and efficiency of physical health assessment of flight students, but also enhances the interpretation ability of the model, providing more personalized and accurate health improvement suggestions for flight students. Through the cross-validation of the improved PCA model, it is proved that the method has high effect and accuracy in practical application, indicating that it is an effective tool to evaluate the physical health of civil aviation flight students. The method of this study provides a new technical support for the health management of civil aviation flight personnel, and helps to improve flight safety and efficiency.