Application of Machine Learning Methods Based on LAZE Priors to Cancer Data——Take the Prostate Cancer Data Set for Example

A sparse model according to the characteristics of high-dimensional cancer data is constructed, the reasons why existing sparse methods can achieve sparse results is analyzed, and based on this, the Bayesian method using LAZE prior is improved, and two new sparse methods suitable for cancer data are obtained: Bayesian method using semi-mixed prior and Bayesian method using complete LAZE prior. The feasibility and effectiveness of the two new sparse methods obtained in this study are verified by using prostate cancer dataset. Numerical experimental results show that compared with traditional sparse methods, the new sparse methods can not only get better sparse effect and completely remove clinical indicators unrelated to the target indicators but also decrease errors.