Improving HIV Treatment Adherence with Predictive Analytics


ART Status Prediction Analysis

SACHI (Society for the Advocacy of Change and Inclusion)

Funding Partner: U.S. Centers for Disease Control and Prevention (CDC)


Data Science Head: Christian ALIYUDA 


Project Overview:

In partnership with the CDC, SACHI developed the ART Status Prediction Analysis project to improve antiretroviral therapy (ART) outcomes for key populations living with HIV/AIDS. By using machine learning models, we are able to predict the likelihood of ART default and take proactive measures to support at-risk patients.


Key Contributions:

This project was led by our team of data scientists  led by Christian,who developed a predictive model to assess ART adherence risks and provided actionable insights for targeted interventions. The project helps SACHI’s healthcare workers intervene early, providing tailored support to those most in need.


• Data Science Contribution: Predictive modeling using Python and Scikit-Learn to forecast treatment adherence risks.

• Impact: Reduced ART discontinuation rates by 15%, resulting in improved care and health outcomes for over 5,000 individuals across Delta State.


Explore the Project https://github.com/ChristianAliyuda/ART-Status-Prediction-Analysis)

Full details of the methodology and code can be accessed in the GitHub repository.