HealthHub is a digital utility designed to support United Nations SDG 3: Good Health and Well-being. It specifically addresses the lack of accessible, judgment-free information regarding sexual and reproductive health.

The application serves as a comprehensive resource featuring an algorithmic chatbot, a decision-tree based self-assessment tool, and a community forum, ensuring that users have access to medically accurate advice and peer support.

• Goal 3: Ensure healthy lives and promote well-being for all at all ages.
• Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes.

This project utilizes core DSA concepts to ensure efficient data retrieval and logical flow, strictly adhering to the complexity analysis in our documentation:

• Application: The responses array stores objects containing regex patterns and answers.
• Logic: The agent iterates through the array sequentially, matching the user's input string against the regex patterns until a match is found.
• Complexity: O(N) (Linear Time), where N is the number of predefined patterns. This was chosen over complex NLP for reliability and simplicity given the fixed dataset.

• Application: The "Diagnostic Engine" uses a Non-Linear Data Structure (Tree).
• Logic: The algorithm traverses from a root node through "Yes/No" branches (parent → child node). Each user choice eliminates a subtree of possibilities until a leaf node (the Result) is reached.
• Complexity: O(H) (Height of the Tree). This allows for rapid triage without evaluating every possible condition.

• Application: The Forum posts are dynamically organized.
• Logic: We implement a sorting logic that compares post objects by their "Likes" count or "Timestamp".
• Complexity: O(N log N). This ensures that the most relevant or popular community advice bubbles to the top efficiently, even as the number of posts grows.

• Application: The Educational Content Library.
• Logic: Static content files are indexed by unique IDs (e.g., modal-101). When a user clicks "Learn More," the system retrieves the data instantly using the ID as a key.
• Complexity: O(1) (Constant Time). This eliminates latency, ensuring instant access to medical definitions regardless of library size.

1. Download or Clone this repository.
2. Navigate to the code/ folder.
3. Double-click index.html to open it in any modern web browser (Chrome, Edge, Firefox, Safari).
   • Note: No installation or server is required.

Group: Sovereign

This project is submitted for CC105 - Data Structures and Algorithms and is available for educational purposes.