Survival prediction models for people living with HIV based on four machine learning models.

ABSTRACT: Although antiretroviral therapy has prolonged the lifespan of people living with HIV, significant variations still exist in survival rates and risk factors among these people. This study compares the performance of the Cox proportional hazard models with four machine learning models in predicting the survival of people living with HIV, analyzing the survival factors among them, thereby assisting medical decision-making. We collected data on 676 people living with HIV from the Chinese Center for Disease Control and Prevention. Significant variables (p < 0.05) were identified using Cox univariate analysis. Using a random number method, the data were split into a training set (473 cases) and a test set (203 cases) in a 7:3 ratio. We employed the Cox proportional hazard model and four classifi

SUBMITTER: Cai Q 

PROVIDER: S-EPMC12378378 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature