An Optimal Model for Medicine Preparation Using Data Mining

Introduction: Lack of financial resources and liquidity are the main problems of hospitals. Pharmacies are one of the sectors that affect the turnover of hospitals and due to lack of forecast for the use and supply of medicines, at the end of the year, encounter over-inventory, large volumes of expired medicines, and sometimes shortage of medicines. Therefore, medicine prediction using available retrospective data leads to improved resource management in hospitals. Due to the high capability of data mining in modeling medical problems, selected algorithms were used to determine the optimal model of medicine preparation.   Method: In this cross-sectional study, to investigate different types of data mining algorithms, an information form was developed based on the design objectives and then defined in the form of reports in the hospital information system. The data were extracted using Crystal Report software. To develop the model, the accuracy of the data mining prediction algorithms including KNN, SVM, NN, Random Forest, LR, and Adaboost was examined based on MSE, RMSE, MAE, and R2 criteria in Weka software. Results: Concerning R2, MAE, and RMSE criteria, Adaboost method (0.78, 247, 827) and random forest method (0.6, 1170, 1868) had the highest accuracy compared to other models and reduced the error rate more. Other methods with the above criteria had poorer performance in predicting the research problem. Conclusion: The results of this study indicated that the Adaboost and random forest methods are more accurate than other methods. A small percentage of hospitals plan to manage the preparation of medicines; thus, it is suggested that managers of hospitals and pharmacies use data mining in the management of their respective units.