African Informatics Studies (LIS Focus) | 23 November 2007
Revisiting AI Algorithm Accuracy in Predicting HIV Risk Factors Among Pregnant Women in Malawi: A One-Year Study
M, u, t, h, u, m, a, C, h, i, t, u, w, o, ,, C, h, i, l, u, f, y, a, S, i, p, h, o
Abstract
This study addresses a current research gap in Computer Science concerning Evaluation of Artificial Intelligence Algorithms in Predicting HIV Risk Factors Among Pregnant Women in Malawi: One-Year Accuracy Analysis in Malawi. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Evaluation of Artificial Intelligence Algorithms in Predicting HIV Risk Factors Among Pregnant Women in Malawi: One-Year Accuracy Analysis, Malawi, Africa, Computer Science, replication study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin<em>{\theta}\sum</em>i\ell(y<em>i,f</em>\theta(x<em>i))+\lambda\lVert\theta\rVert</em>2^2$, with performance evaluated using out-of-sample error.