Drug Demand Prediction Based on Epidemiology Factors Using Random Forest
Gunadi Emmanuel, Yulyani Arifin, Ilvico Sonata, Muhammad Zarlis · 2023
The characteristics of the disease that spreads quickly, the number of sufferers, and the severity of sufferers of Coronavirus Disease 2019 are components of uncertainty during the pandemic. In an uncertain situation, prediction models for the need for drugs and medical devices are of great concern to policymakers in government, drug manufacturers, distributors, and pharmaceutical installation managers to maintain drug availability. Drug need prediction models that rely on historical data components on drug use are no longer reliable. Learning from the COVID-19 case, epidemiological variables correlate with predicting drug demand. This research includes data on ten major diseases in private hospital units for 2017–2022 to complete historical data on drug use. This study implements the Random Forest algorithm. The research method uses literature studies and processing field data from pharmaceutical installations. The analysis process uses KNIME software. The level of accuracy in predicting drug demand from historical drug use data was 77.272%, increasing to 81.818% with a model for predicting drug demand based on consumption cycles and classification of drug therapy groups. Furthermore, predictions of drug demand can consider variables recorded in medical records related to the seasonal frequency of diseases.