Data-Driven Pharmaceutical Sales Analysis and Prediction in the Healthcare Industry
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P120Keywords:
Sales Forecasting, Machine Learning, Time Series Analysis, Pharmaceutical Industry, Seasonality EffectsAbstract
Forecasting pharmaceutical drug sales is a significant challenge for healthcare organizations and pharmaceutical companies due to factors such as seasonality, weather conditions, local health crises, import issues, currency fluctuations, and economic instability. These factors can contribute to shortages of drugs or a surplus of drugs, impacting both drug availability and drug operations. The challenges discussed above have been addressed in this study by presenting an efficient model for Pharmaceutical Sales Analysis and Forecasting using a combination of Hybrid Random Forest (RF) and Gated Recurrent Unit (RF+GRU) model on the Pharmaceutical sales dataset available from Kaggle. The data was preprocessed using techniques like outlier detection, handling missing data, label encoding, normalization, feature extraction, and data balancing using SMOTE to improve data quality and predictive accuracy. The hybrid model proposed is built by combining the best of both worlds from the feature extraction capabilities of RF and the sequential learning capability of GRU in order to increase forecast accuracy and minimize forecast error. A 96.1% accuracy (ACC) rate, 96.8% precision (PRE), 96% recall (REC), and 96.4% F1-score (F1) were attained by the suggested RF+GRU model in comparison to the other models, including XGBoost, SVM, and KNN, according to the experimental data. The results demonstrate that the hybrid forecasting model is reliable and effective for predicting sales for the pharmaceutical industry, and can be utilized to support the inventory management system and healthcare decision-making system.
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