Medication Adviser System

Abstract

In the realm of healthcare, timely and accurate diagnosis, coupled with appropriate medication, is crucial for effective treatment and patient well-being. This paper presents an innovative Medication Adviser System that leverages Natural Language Processing (NLP) to identify diseases based on user-reported symptoms and subsequently recommend appropriate medications. The system is designed to streamline the preliminary diagnostic process and provide immediate guidance on potential treatments, especially in situations where access to professional medical advice may be limited or unavailable. By integrating advanced NLP techniques, the system enhances the accuracy of symptom interpretation and disease identification, making it a valuable tool for early diagnosis and self-care. The core functionality of the system involves processing user-inputted symptoms through an NLP model, which is trained to recognize and correlate symptoms with a comprehensive database of diseases. This involves several steps, including data cleaning, feature extraction using techniques like TF-IDF Vectorizer, and matching symptoms with existing medical records. Once the system identifies the most likely disease, it retrieves detailed information such as a description of the disease, a list of recommended medications, and preventive measures. Additionally, it provides users with suggested workouts and dietary plans that can help manage their condition and maintain overall health. This system offers a user-friendly platform for individuals seeking quick medical insights. It acts as a temporary solution to address health concerns and supports preventive care by offering actionable recommendations. The Medication Adviser System thus bridges the gap between symptom recognition and medical advice, enhancing healthcare accessibility and promoting early intervention.