Repository for Team 07 – Vibe Coding Hackathon
Track: Healthcare
Problem Statement: PS 05 – “Silent Disease” Early Detection Engine
Silent diseases often progress without noticeable symptoms during early stages, leading to delayed diagnosis and reduced treatment effectiveness. This project addresses this challenge by developing an AI/ML-based early risk detection system that analyzes lifestyle, behavioral, and health-related data to identify potential risk indicators before clinical symptoms appear.
The solution focuses on preventive healthcare and early awareness, enabling timely intervention and improved long-term patient outcomes.
To design and develop an intelligent, non-diagnostic system capable of identifying early warning signs of silent diseases using data-driven analysis, supporting proactive and preventive healthcare decision-making.
The system leverages machine learning and rule-based inference techniques to:
- Analyze structured and semi-structured health and lifestyle data
- Detect hidden or emerging risk patterns over time
- Generate risk scores and confidence levels
- Explain contributing factors behind identified risks
The solution is designed to be interpretable, scalable, and adaptable to real-world healthcare scenarios.
- Data Collection – Lifestyle, behavioral, and health-related inputs
- Preprocessing Layer – Cleaning, normalization, feature extraction
- Risk Analysis Engine – Pattern detection and risk scoring
- Inference Layer – Early warning classification (Low / Medium / High)
- Output Layer – Risk insights, contributing factors, and explanations
(Detailed architecture is documented separately.)
- Programming Language: Python
- AI / ML: Scikit-learn / TensorFlow / PyTorch
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib / Seaborn
- Version Control: Git & GitHub
(Final stack may evolve during development.)
https://drive.google.com/drive/folders/1l9L1IlXdU0Mj4jC-GiVWtMiMLo1liK7r?usp=sharing
This system does not provide medical diagnosis.
It identifies early risk indicators based on lifestyle and behavioral data to support preventive healthcare awareness only.