Abstract:
HIV and AIDS remain prominent global health concerns and antiretroviral medication (ART) plays a crucial role in
treating infected individuals, preventing disease progression, and improving overall health outcomes. However,
missed appointments in ART programs pose significant challenges by causing treatment interruptions, unsuppressed
viral load, and increased HIV transmission rates. This research employs the CRISP-DM methodology and aims to
develop a predictive model that effectively reduces missed appointments among people living with HIV. A
comprehensive analysis of patient data, including demographics, clinical information, and appointment history, was
conducted to determine the key factors influencing missed appointments. The prediction model was developed using
decision trees, random forests, and support vector machines. The study found that decision trees produced the best
results, having lower square errors and greater R squared. The findings contribute to the advancement of predictive
analytics in healthcare, particularly in the context of chronic conditions such as HIV/AIDS.
