ISHACARE

Inspiration Healthcare reports are often complex and difficult for non-medical users to understand. Many people receive lab reports filled with numbers and medical terms but struggle to interpret what those values actually mean for their health. This gap between medical data and user understanding inspired us to build ISHACARE — a system that simplifies medical reports using AI and makes health insights accessible to everyone.

What it does ISHACARE is an AI-powered health intelligence system that analyzes medical reports and provides instant insights. Users can upload a report (PDF or image), and the system: Extracts key health parameters automatically Calculates a health score Performs risk analysis for diseases like diabetes, heart conditions, anemia, etc. Uses machine learning to predict overall health risk Provides personalized recommendations Generates a downloadable AI-powered health report with visualizations

How we built it We designed ISHACARE using a modular architecture: Frontend: Built using streamlit for an interactive dashboard Backend: FastAPI for handling API requests and processing Machine Learning: Scikit-learn model trained on health parameters like glucose, cholesterol, and hemoglobin Data Extraction: PDF and image processing to extract structured data Visualization: Plotly and Matplotlib for graphs Report Generation: reportLab for generating downloadable PDF reports We used a hybrid approach, combining: Rule-based medical logic (for reliability) Machine learning (for predictive insights)

Challenges we ran into Extracting structured data from different medical report formats was difficult due to inconsistent layouts Ensuring compatibility across PDF and image inputs required handling multiple edge cases Balancing rule-based logic with machine learning predictions for better accuracy Deployment challenges, especially handling dependencies and model integration in cloud environments

Accomplishments that we're proud of Successfully built an end-to-end AI system that works in real-time Integrated both rule-based and ML-based analysis Designed a clean and user-friendly interface Implemented automated report generation with visual insights Deployed the backend on cloud infrastructure

What we learned Practical implementation of FastAPI and Streamlit integration Building and deploying machine learning models Handling real-world data inconsistencies Designing scalable and modular system architecture Importance of user experience in technical applications

What's next for ISHACARE Adding an AI chatbot to explain reports conversationally Supporting more diseases with advanced ML models Improving accuracy using real clinical datasets Adding user authentication and report history tracking Integrating wearable health device data Enhancing OCR for better compatibility with all report formats

Impact ISHACARE aims to make healthcare more accessible, understandable, and proactive. By providing instant insights and early warnings, it empowers users to take better control of their health and make informed decisions.

Built With

• fastapi
• matplotlib
• plotpy
• pytesseract
• python
• streamlit