Prediction of Anti-Retroviral Drug Consumption for HIV Patient in Hospital Pharmacy using Data Mining Technique

Pharmacy handles all the medicine needed in the hospital that consists of vast amount of records. These produce large scale of datasets that are complex to manage and thereby need tools and technique to easily process, interpret, forecast and predict future consumption. Due to this, the method of predicting and forecasting stock consumption using Data Mining technique in hospital pharmacy is not be a surprising issue. Thus, this research investigated the potential applicability of data mining technology to predict the Anti-Retroviral drugs consumption for pharmacy based up on patient's history datasets of Jugal hospital, Harar, Ethiopia. The methodology used for this research is based on Knowledge Discovery in Database which had mostly relied on using the decision tree algorithms specifically M5P model tree. WEKA software, a data-mining tool were used for interpreting, evaluating and predicting from large datasets. Result with the data set suggests that tree based modeling approach can effectively be used in predicting the consumption of ARV drugs.