Drug Recommendation System Based on Symptoms
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Abstract
The integration of digital health technologies has transformed patient care by enabling the development of intelligent systems that assist in medical decision-making. This paper introduces a Drug Recommendation System (DRS) designed to analyze user-inputted symptoms and recommend appropriate medications. Utilizing advanced Natural Language Processing (NLP) techniques, the system preprocesses and classifies textual symptom data, facilitating accurate drug suggestions. The implementation of machine learning algorithms, particularly the Multinomial Naive Bayes classifier, allows for the effective prediction of suitable medications based on historical symptom-drug associations. This research underscores the potential of DRS in enhancing clinical workflows by reducing the cognitive load on healthcare providers and improving patient safety through tailored medication recommendations. Furthermore, the system's user-friendly interface ensures accessibility, empowering patients with knowledge about their conditions and potential treatments. By harnessing the power of data-driven insights, this study aims to contribute to the evolution of personalized healthcare solutions, thereby improving patient outcomes and satisfaction.
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