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🩺 Medicano – A Smart Medical Assistant

Medicano is a comprehensive AI-powered digital healthcare platform built with Python and Streamlit. It provides real-time health monitoring and personalized tools to assist users in managing critical health conditions such as PCOS, Diabetes, and cardiovascular indicators through RPPG. Medicano is designed to be intelligent, intuitive, and accessible.

Owner: Aman Hasan License: MIT Technology Stack: Python, Streamlit, MongoDB, Twilio, OpenCV, Machine Learning

🚀 Features

🔬 PCOS Management Suite • 🧬 PCOS Predictor – AI-based prediction of PCOS from medical data • 🥗 Diet Plan Generator – Personalized meal plans tailored for PCOS management • 🔍 PCOS Type Classifier – Classifies PCOS into types (Insulin-Resistant, Inflammatory, Adrenal, Pill-Induced) using a smart questionnaire

❤️ rPPG (Remote Photoplethysmography) • Real-time heart rate detection from webcam or video • Graph plotting, BPM export to PDF, face-based tracking using dlib

💉 Diabetes Checker • Simple questionnaire-based diabetes prediction with intelligent insights

💊 Medicine Information & Pill Reminders • Get medicine usage, dosage, and precautions • Automated SMS/email pill reminders via Twilio

🧾 Medical Document Vault • Secure upload & tagging of prescriptions, reports, and discharge summaries • User-specific folders with doctor-sharing links

🏥 Nearby Healthcare • Locate nearby pharmacies and ambulance services using geolocation

📈 Tech Highlights

• Streamlit UI – Dynamic dashboards with animations and styling • Machine Learning – Logistic regression, decision trees, or pretrained classifiers • Computer Vision – Face detection, signal processing, PPG-based HR analysis • Twilio API – SMS/email alerts for medication reminders • MongoDB Atlas – Secure data storage and retrieval

SCREENSHOT:

Screenshot 2025-07-15 at 11 16 53 AM

👤 Author Aman Hasan 📧 amanhasan254@gmail.com 🌐 LinkedIn 🐙 GitHub

🧠 Future Scope

• Face-based oxygen level and stress detection • Integration with wearable devices • Teleconsultation modules • Mental health survey & analysis

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