Enhancing Drug Recommendations: A Modified LSTM Approach in Intelligent Deep Learning Systems

Abstract

The overwhelming burden on the healthcare system has resulted in increased mortality rates as individuals struggle to receive appropriate medical attention. Machine learning has demonstrated its efficacy across diverse applications, fueling a growing trend in the realm of innovative automation. This is particularly facilitated by drug recommendation systems, which possess the capability to significantly reduce the dependence on specialized assistance. Faced with limited access to medical expertise, a considerable number of individuals have turned to self-medication, often worsening their health conditions. In response to this critical scenario, this paper introduces an inventive drug recommendation system which helps to lessen the strain healthcare professionals. Here, we present the development of a medication recommendation system based on LSTM, utilizing user-provided symptoms and the model's evaluations of past drug recommendations. The results of the comparison between Random Forest, SVM, and LSTM indicated that the modified LSTM-based recommendation system outperforms the other two models, achieving a 94% accuracy rate.