An Artificial Intelligence-Based Random Forest Model for Reducing Prescription Errors and Improving Patient Safety.
| dc.contributor.author | Maphosa, V. | |
| dc.contributor.author | Mpofu, B. | |
| dc.date.accessioned | 2025-07-15T13:21:05Z | |
| dc.date.available | 2025-07-15T13:21:05Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Pharmaceutical intake is crucial for alleviating illnesses and extending patients’ lives. However, frequent medication errors lead to catastrophic injuries or death. Developed countries have adopted artificial intelligence (AI) systems to enhance patient safety. This research aimed to develop an AI-based model that reduces medical errors by reviewing the patient’s prescriptions and predicting their disease. The prediction gets validated against the doctor’s diagnosis. In cases of inconsistencies, the patient undergoes a review process. This study reviewed medication errors and AI systems. The study utilized an experimental design methodology with a dataset of 797 observations containing diseases, symptoms, medications and other variables from the Kaggle database. The model was built using the Random Forest and the decision tree’s train-test-split technique. Python libraries including Pandas, NumPy, Matplotlib, and Seaborn were used for data manipulation. The model was evaluated using classifications, correlations and the confusion matrix, achieving 83.33% accuracy. The model holds significant potential for governments and healthcare professionals to reduce medication errors. The study’s limitations include the use of secondary data. Future studies should consider using additional variables such as age, environment, and gender. Increasing dataset observations will enhance the model’s accuracy. Implementation and ethical considerations are necessary to ensure patient safety. | |
| dc.identifier.citation | Maphosa, V. and Mpofu, B., 2024. An Artificial Intelligence-Based Random Forest Model for Reducing Prescription Errors and Improving Patient Safety. Available at SSRN 4842105. | |
| dc.identifier.uri | http://196.220.97.103:4000/handle/123456789/980 | |
| dc.language.iso | en | |
| dc.title | An Artificial Intelligence-Based Random Forest Model for Reducing Prescription Errors and Improving Patient Safety. | |
| dc.type | Article |