Research Article
Asasira Obed
Abstract
One of the most important indices of defining general welfare and quality-of-life of people in the world is physicaland mental health of individuals.Health care managers and planners therefore must make future demand for healthcare services and the need formedicines to achieve fully and reliable supply. With the introduction of the test and treat methodology of managing HIVpatients, first line Antiretroviral drugs (ARVs) must be in adequate availability to enable facilities implement this strategyof HIV eradication. Discontinuation of antiretroviral therapy Antiretroviral drugs (ART) due to shortages may result intoviral rebound, immune decomposition, and clinical progression of the virus, therefore there is need to plan ahead oftime to avail the most required stock for ARV drugs.There are no proper forecasting and anticipation mechanisms of future demand for first line Antiretroviral drugs(ARV) and this is a cross cutting problem for all the public health facilities in Mbarara District and this has led tooverstocking and understocking of these drugs leading to shortages and wastage related to expiryThis study aimed at designing a predictive model for demand of first-line ARV drugs in Mbarara district, usingdata mining techniques. Using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, theobjectives of this study were to extract and prepare dataset required for data mining, examine different methods usedin demand prediction, Design a model for predicting demand and evaluate this model.The model was trained under the Waikato Environment for Knowledge Management (WEKA) which is a data miningenvironment and predicted the demand for first line ARVs in various health facilities Mbarara district Uganda. The testresults showed that the forecasting in time series approach was more suitable and efficient for drug cycles aheaddemand forecasting. Forecast results demonstrated that the model performed remarkably well with increased numberof actual data and iterations. A regression model gave more accurate forecast results with 7.3% Mean PercentageError as compared to alternative methods of demand forecasting whose error was above 30%.