Revolutionizing Digital Healthcare: An AI-Based Symptom Prediction and Treatment Advisory System

This study explores development and implementation of a personalized, AI-driven health advisory system designed to support individuals in identifying potential illnesses based on self-reported symptoms. One of the major challenges in early-stage diagnosis is the lack of accessible, reliable, and real-time tools that can guide users before they seek professional care. The system integrates a machine learning-based symptom checker with interactive healthcare features, delivering real-time disease predictions along with detailed information, medication suggestions, preventive measures, and lifestyle guidance. By combining health prediction technology with user-friendly interface design through Streamlit, the platform enhances engagement and ease of use. This initiative presents a multi-functional health support system that aims to empower users with essential knowledge and promote preventive healthcare practices. The AI model was trained on medical datasets and demonstrated a disease prediction accuracy of over 95 percent, enabling it to suggest relevant outcomes with high reliability. Experimental results highlight the model’s capability to provide accurate guidance, making it a supportive tool in both self-care and early intervention.