Authors - Puja Cholke, Om Yogesh Suhagir, Maroof Mustaq Mohammed Gadiwale, Srushti Pancham Mane, Sanika Suresh Mohite, Shreya Ramesh Phalke Abstract - Snake bites pose a severe public health risk, especially in rural and tropical regions, where delayed treatment often leads to fatalities. Existing systems struggle to classify snakes accurately based on symptoms, causing delays in administering the correct antidote. To address this issue, BiteSage (Snake Bite Antidote Suggester) utilizes data science and machine learning to classify snake bites as venomous or non-venomous based on user-reported symptoms and recommend the appropriate antidote. A chatbot interface assists users in symptom formulation and provides real-time counseling. Additionally, the system offers visualization tools to analyze global trends in snake bites, enhancing awareness and preparedness. The model ensures high precision in bite classification and antidote recommendations, backed by comprehensive data analytics. This research benefits medical professionals in remote areas and educates the public, helping to reduce fatalities and improve emergency response. By integrating AI-driven analysis, real-time assistance, and data visualization, BiteSage enhances medical decision-making and public awareness, ultimately saving lives.
