I've created a comprehensive Emergency Decision Support System that simulates machine learning-powered emergency decision making. The system features three main emergency scenarios (traffic accidents, medical emergencies, and fire emergencies) with intelligent decision trees that provide actionable recommendations.
The application uses a sophisticated decision engine that simulates how a real scikit-learn model would work, incorporating weighted scoring, confidence calculations, and contextual reasoning. Each scenario has carefully crafted questions that feed into the decision algorithm, producing tailored recommendations with urgency levels, step-by-step actions, and important safety warnings.
The interface is designed specifically for emergency situations with high contrast, clear typography, and mobile-first responsive design. The system provides immediate access to emergency calling functionality and maintains focus on user safety throughout the experience.## Inspiration
Built With
- algorithms
- and
- architecture:
- aria
- calculation
- class
- components
- confidence
- css3
- custom
- decision
- engine:
- external
- features
- functional
- html
- html5
- key
- logic:
- lucide
- mimicking
- ml
- mobile-first
- navigation
- no
- patterns
- postcss
- react
- responsive
- router)
- rule-based
- scikit-learn
- simulating
- simulation
- state-based
- system:
- tailwind
- tree
- typescript
- vite
- weighted
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