Lluminor
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My Cardio

CommissionWebHealth2025 – 2025

Overview

A commission health monitoring web app focused on cardiac health. Users log their health data and receive AI-assisted and algorithm-based consultation feedback. Built with React and TypeScript on the frontend, Node.js and PostgreSQL on the backend. The consultation logic combines rule-based algorithms with AI-generated responses to give users meaningful health insights without replacing professional medical advice.

A commission health monitoring web app focused on cardiac health — built for patients to log health data and receive structured consultation feedback.

The consultation logic is a hybrid: rule-based algorithms handle deterministic health thresholds (blood pressure ranges, heart rate zones), while AI-generated responses add context and nuance beyond what rules can express.

Frontend in React and TypeScript, backend in Django with PostgreSQL. Containerized with Docker for consistent deployment across environments.

Built with care around what AI should and shouldn't do in a health context — the system gives users meaningful insights without positioning itself as a replacement for professional medical advice.

Stack

UI / Frontend

React

Backend / API

DjangoPython

Database

PostgreSQL

Infra / Deploy

Docker

Inspiration

A real client needed a tool that could help patients understand their cardiac health data between doctor visits — without making clinical claims.

Structure

Architecture

Client
React
API
Django REST API
Logic
Rule EngineAI Consultation Layer
Data
PostgreSQL

Schema

User
→ HealthProfile (1:1)
HealthProfile
agenumber
weightnumber
→ Readings (1:N)
Readings
bloodPressurenumber
heartRatenumber
symptomsstring
→ Consultations (1:N)
Consultations
ruleTierstring
aiResponsestring

How it works

01

User Registration

Account creation with health profile (age, weight, medical history flags).

02

Health Data Logging

Input forms for blood pressure, heart rate, symptoms, and daily metrics.

03

Rule-Based Analysis

Threshold algorithms classify readings into risk tiers (normal, elevated, critical).

04

AI Consultation

AI generates contextual feedback based on the classified data and patient history.

05

Feedback Display

Patient-facing summary with insights, trend charts, and recommended actions.

06

Deploy

Dockerized Django + PostgreSQL stack deployed to client infrastructure.

Jamil Harun · 2026