Beschreibung
Pharmaceutical industries mostly produce huge amounts of datasets to build a basis for prediction, forecasting, and classification. Specifically, when applying predicting and forecasting on pharmaceutical industries for stocking management system will yield huge amounts of datasets. The application of data mining tools will be the best tools for predicting and forecasting of pharmaceutical industries on stocking/stock management system. Thus, this research work has investigated the potential applicability of data mining technology to predict the Anti-Retroviral (ARV) drugs consumption for pharmacy based up on patients history datasets gathered from the hospital pharamcy. WEKA software, a data-mining tool were used for interpreting, evaluating and predicting from large datasets. It has been concluded that the M5P decision tree algorithm and data mining techniques are the best and crucial component for the purpose of prediction and forecasting of ARV drugs consumption for the Jugal hospital pharmacy. Result with the data set suggests that tree based modeling approach can effectively be used in predicting the consumption of ARV drugs.
Autorenporträt
Dr. Patrick D. Cerna is an Assistant Professor in College of Computing and Informatics in Haramaya University. He is a member for some of the prestigious IS/IT international organization e.g IEEE, ACM, AISnet, IACSIT, and IAES. He has published several research papers in reputable journal publication and served as Editorial board as well.
