Reliable Drug Recommendations using a Hybrid CNN-BiLSTM and Whale Algorithm-Optimized Aspect-Based Sentiments

Abstract

The growing availability of drug reviews from patients has paved the way for future patient-centric and data-enabled healthcare support; however, the majority of existing sentiment-based drug recommendation systems use overall sentiment (sentiment polarity) to generate drug recommendations. In this study, an aspect-aware and optimized deep-learning-based drug recommendation system is proposed using sentiment analysis. A combined Convolutional Neural Network and Bidirectional Long Short-Term Memory architecture is employed to leverage important local semantics as well as contextual and sequential dependencies from drug reviews. The model is extended into an explainable drug recommendation system that maps user-reported symptoms to patient-opinion-informed drug rankings. Sentiments are analyzed at the aspect level, focusing on effectiveness, side effects, dosage, safety, and cost. To achieve better results, the Whale Optimization Algorithm is used to assign optimal weights to each aspect, thereby ranking drugs according to data-driven aspect preferences learned from patient reviews. Experimental evaluation on the drug dataset, comprising patient reviews, demonstrates that the proposed hybrid sentiment classification model achieves 99.80% accuracy. It outperforms traditional machine learning and deep learning approaches. The proposed framework not only improves sentiment classification accuracy but also provides transparent and improved top-k drug recommendations, offering explainable patient-opinion-informed recommendation rankings derived from large-scale review data. The generated rankings are derived from patient-generated review data and should therefore be interpreted as opinion-aware recommendations rather than clinically validated treatment recommendations.