Development of a Disease Diagnosis Chatbot using Machine Learning Techniques

Abstract

The Disease Diagnosis Chatbot Based on Machine Learning Methods is a medical tool that seeks to provide preliminary disease diagnoses according to user-input symptoms. It uses a trained Random Forest model in combination with a systematically organized and pre-processed medical database to search symptom patterns and predict probable medical illnesses with high accuracy. Users can enter a maximum of two symptoms using an easy-to-use web interface, and the system gives feedback on the most probable disease according to its training data. Built with Streamlit, the chatbot supports an easy and trouble-free user interface, even for nontechnical users. The model works in real time, providing instant results and helping the user make the decision to seek a professional doctor. Such functionality makes it especially useful in remote or undeveloped areas where healthcare is not easily accessible. By acting as decision-support system, the chatbot makes common diseases accessible and promotes early health interventions. Future work could include adding more symptom data, using deep learning models for better accuracy, and creating integrations with telehealth services to provide a more complete digital health solution. This research is a significant step towards increasing access to healthcare through machine learning-based automation and fruitful digital innovation.

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