Health Diagnosis System

Project Description:

I've developed a healthcare diagnosis assistant that enables users to input their symptoms and receive real-time disease predictions. This intuitive system harnesses the power of natural language processing and machine learning to offer quick, reliable healthcare guidance.

System's Purpose:

The purpose of this system is to simplify healthcare decision-making. It empowers users to assess their health by providing rapid disease predictions based on their symptoms. By bridging the information gap between individuals and medical knowledge, this tool contributes to informed self-assessment and early disease recognition.

Challenges Solved:

The development process of this healthcare diagnosis system required tackling various challenges. The system has achieved a remarkable feat by being able to analyze symptoms provided by users, predict potential diseases, and encourage user participation in healthcare management. This project also offers potential applications in various fields, from telemedicine to health research.

Key Features:

• Symptom Analysis: The system analyzes user-entered symptoms to predict potential diseases.
• Early Detection: Users receive early warnings about possible medical conditions based on their symptoms.
• User Empowerment: Empowers individuals to actively manage their health and make informed decisions.

Applications:

This system has diverse applications, including:

• Telemedicine and Self-Assessment: Enabling users to self-assess their health and make informed healthcare decisions.
• Healthcare Awareness: Promoting health awareness by providing insights into potential diseases.
• Early Disease Recognition: Supporting early diagnosis and treatment by alerting users to possible health issues.
• Health Research: Contributing to medical research by collecting anonymized symptom data.

Tools Used:

• Programming Language: Python
• Machine Learning Models: Employed machine learning models for symptom-disease prediction.
• Natural Language Processing: Utilized NLP techniques for text analysis.
• User Interface: Integrated Streamlit for a user-friendly experience.

Demo

https://pontonkid-healthcare-recommender.hf.space