Advanced drug recommendation using long short-term memory and type-2 fuzzy logic integration

Abstract

This research on hybrid models for drug recommendation systems proposes long short-term memory (LSTM) and type-2 fuzzy logic (T2FL) to make its recommendations more accurate and reliable. The model leverages LSTM's ability to capture temporal patterns in medical data while addressing the inherent uncertainty through T2FL. Evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R²), accuracy, precision, recall, F1-Score, and area under the curve-receiver operating characteristic (AUC-ROC) demonstrate that the proposed model significantly outperforms traditional models like LSTM without fuzzy, linear regression, and random forest. Integrating these two methods results in more accurate and consistent predictions, making the model highly effective in handling complex and uncertain data. Practical implications include the potential for improving personalized treatment plans and patient outcomes in clinical settings. Future research directions involve applying this hybrid approach to larger, more diverse datasets and exploring additional hybrid methods that enhance prediction accuracy and model robustness. The findings suggest that the LSTM+T2FL model is a promising tool for advancing drug recommendation systems in the medical field.