An Intelligent Medical Chatbot for Symptom Severity Classification and Personalized Recommendation Using Random Forest

The rapid expansion of digital health technologies has created a pressing need for intelligent, accessible triage and diagnostic assistance systems. This paper presents a Medical Chatbot platform that leverages machine learning to interact with patients, classify symptom severity, and provide comprehensive medical recommendations. The system utilizes a conversational framework built on Streamlit with text-based symptom ex- traction, allowing seamless analysis of self-reported symptom intensities. By employing a Random Forest Classifier trained on synthetic medical data infused with Gaussian noise, the system accurately categorizes conditions into Normal, Moderate, or High severity levels. The platform further integrates a secure SQLite- backed authentication module with bcrypt password hashing and a rule-based recommendation engine for medication, diet, and appropriate precautions spanning Ayurvedic, Homeopathy, and Allopathy treatment methodologies. Experimental evaluation demonstrates balanced training and testing accuracies (averaging 75%–80%), indicating robust generalization without overfitting. The proposed system bridges the gap between initial symptom onset and formal medical consultation by providing continuous, data-driven medical triage. Results confirm that the integration of machine learning within interactive chatbots significantly enhances accessible preliminary healthcare.