A Machine Learning Approach to Predict HIV Viral Load Hotspots in Kenya Using Real-World Data.

ABSTRACT:

Background

Machine learning models are not in routine use for predicting HIV status. Our objective is to describe the development of a machine learning model to predict HIV viral load (VL) hotspots as an early warning system in Kenya, based on routinely collected data by affiliate entities of the Ministry of Health. Based on World Health Organization's recommendations, hotspots are health facilities with ≥20% people living with HIV whose VL is not suppressed. Prediction of VL hotspots provides an early warning system to health administrators to optimize treatment and resources distribution.

Methods

A random forest model was built to predict the hotspot status of a health facility in the upcoming month, starting from 2016. Prior to model building, the datasets were cleaned an

SUBMITTER: Kagendi N 

PROVIDER: S-EPMC10880164 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature