Machine learning algorithms to predict the risk of hyperlipidemia in people with HIV after starting HAART for 6 months.

ABSTRACT:

Objective

The purpose of this study was to use machine learning models to predict the risk of hyperlipidemia in people with HIV (PWH) for 6 months after starting HAART, to improve early intervention efforts and prevent further progression to cardiovascular and cerebrovascular diseases.

Methods

This study enrolled HAART-naive individuals who visited the clinic at Beijing Ditan Hospital between January 2015 and January 2023. All clinical features were extracted from the electronic medical records. A classification prediction model was established based on various machine learning algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to predict the risk of hyperlipidemia based on accuracy, positive-predict

SUBMITTER: Ding Y 

PROVIDER: S-EPMC12337911 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature