Survey Report on MediScope - Web-Based Disease Surveillance using NLP and Machine Learning

People often struggle to access reliable and timely health information about diseases and viruses, as relevant data is scattered across multiple sources such as WHO, health portals, and government websites. This leads to delays in awareness, misinformation, and difficulty in understanding disease symptoms, affected regions, and preventive measures. Current health information systems are typically static and do not provide real-time, consolidated insights. To solve these issues, we propose MediScope: Spot Sickness Before It Spreads, an intelligent healthcare web crawling and analytics system. This system automatically extracts health data from trusted sources using web scraping techniques, processes and analyzes information using Natural Language Processing (NLP) for entity recognition and symptom extraction, and presents insights through interactive dashboards. It uses a combination of web crawling frameworks (BeautifulSoup, Scrapy), NLP libraries (spaCy, NLTK), and data visualization tools to collect, standardize, and display disease information including current outbreaks, symptoms, geographic distribution, and prevention strategies. By offering centralized access, real-time updates, and user-friendly visualization, this solution aims to improve public health awareness, enable early disease detection, and support informed decision-making, providing a powerful tool for modern healthcare information management.