Project Overview:

The AI-Based Symptom Checker is a web-based application designed to help users identify possible medical conditions based on their input symptoms. By using machine learning (ML) and natural language processing (NLP), the system suggests potential illnesses, advises the next steps (self-care, see a doctor, emergency), and optionally connects users to healthcare providers.

This project improves self-diagnosis accuracy and reduces unnecessary doctor visits, especially in remote areas.

Project Objectives:

• To provide a smart, interactive platform for preliminary health diagnosis

• Reduce patient anxiety and medical overload by providing immediate guidance

• Use ML classification models to match symptom patterns to known conditions

• Optionally, guide users to the nearest medical facility or schedule a consultation

Technology Stack:

Frontend:

• HTML, CSS, Bootstrap – responsive design

• JavaScript – interactive UI, dynamic symptom form

Backend (choose one):

• Node.js (Express)

• PHP (Laravel or core PHP)

• Java (Spring Boot or Servlet-based)

AI/ML Integration:

• Python-based ML model via Flask API (called from Node/PHP/Java backend)

• Use models like Naive Bayes, Random Forest, or Logistic Regression

Database:

• MySQL / MongoDB – to store symptoms, users, and result logs

Optional APIs:

• Google Maps API – to suggest nearby clinics

• Chatbot API – for conversational symptom checking