Project Description:

The AI-Based Medical Diagnosis Assistant is an intelligent web application that uses Machine Learning and Natural Language Processing (NLP) to help users (patients or healthcare professionals) analyze symptoms, suggest potential medical conditions, and recommend next steps such as consulting a specialist, taking a test, or following home remedies.

This system is not a replacement for a doctor, but it acts as a first-level screening tool or pre-diagnosis helper using AI algorithms trained on real-world medical data.

Core Objective:

To create an AI-driven system that can analyze user-inputted symptoms and predict possible medical conditions, enhancing accessibility to health insights and encouraging timely medical action.

Key Features:

1. Symptom Input via Text or Form:

   • Users can type symptoms (e.g., "fever, sore throat, cough") or select from a list.

   • NLP engine interprets natural language inputs.

2. AI Diagnosis Prediction:

   • Uses ML models trained on symptom-disease datasets.

   • Predicts the top 3–5 possible conditions with confidence scores.

3. Specialist Recommendation:

   • Suggests which type of doctor to consult (e.g., ENT, General Physician).

4. Initial Advice:

   • Offers basic advice, home remedies, or urgency level (low, medium, emergency).

5. Medical History Integration (Optional):

   • Users can log previous conditions for improved accuracy.

6. Voice Input (Optional):

   • Allow symptom entry via speech (using JavaScript Web Speech API).

7. Disclaimer & Legal Compliance:

   • Clear disclaimer: Not a substitute for professional medical advice.

Tech Stack:

 Backend:

• Node.js (Express) / Java (Spring Boot) / PHP (Laravel)

• ML model integration using Python (Flask API) or TensorFlow.js

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript (jQuery, form validation)

• Optional: Chart.js or D3.js for visualization

 AI/ML:

• Symptom-checking ML model using:

  • Decision Trees / Random Forests / Naive Bayes

  • Trained on datasets like Disease Symptom Table, MedlinePlus, or openFDA

 Database:

• MySQL / MongoDB

• Stores symptoms, predictions, user sessions