Abstract:
Tuberculosis (TB) as a co-infection in People Living with HIV (PLHIV) is a serious public
health concern, especially in underdeveloped nations like Pakistan. Robust data
harmonization and sophisticated machine learning approaches are essential for the efficient
management and treatment of chronic diseases. This work uses national health data from
the Ministry of Health Pakistan to provide a machine learning-based method for detecting
TB as co-infection in PLHIVs. To train different machine learning models, we used an
extensive dataset that included patient demographics, clinical history, behaviors, lab
results. The machine learning models trained on the extensive data set we used accuracy,
recall, precision, and F1-score and Area Under Curve (AUC) parameters to assess the
efficiency of models. According to our findings, machine learning methods can greatly
improve the identification of TB co-infection in HIV patients, giving public health
professionals a useful tool for tracking and containing the spread of these illnesses. Real
time dashboard, data analysis and decision-making are made easier and disease detection
accuracy is increased when machine learning algorithms are integrated with national health
databases. This study emphasizes how machine learning can revolutionize disease
management and public health surveillance in environments with limited resources.
