Final-Project

PCOS Prediction and Health Management System

Overview

The PCOS Prediction and Health Management System is a web-based application developed to assist users in predicting the risk of Polycystic Ovary Syndrome (PCOS) using machine learning techniques. The system also provides health management features such as visualization, symptom diary management, chatbot assistance, and secure user authentication.

The application is developed using Python Flask, Scikit-learn, MySQL, HTML, CSS, and Bootstrap.

Features

• User Registration and Login
• Machine Learning-Based PCOS Prediction
• Probability-Based Risk Analysis
• Data Visualization
• Chatbot Assistance
• Symptom Diary Management
• File Upload Functionality
• Secure Database Integration
• Admin Management Features

Technologies Used

Backend

• Python
• Flask

Machine Learning

• Scikit-learn
• Pandas
• NumPy

Frontend

• HTML
• CSS
• Bootstrap

Database

• MySQL

Machine Learning Model

The system uses a Random Forest classification model for predicting PCOS risk based on medical and lifestyle-related data.

Evaluation metrics used:

• Accuracy
• Precision
• Recall
• F1-Score

Project Structure

project/
│
├── .vscode/
├── __pycache__/
├── static/
├── templates/
├── uploads/
│
├── .env
├── app.py
├── feature_names.pkl
├── pcos_model.pkl
├── requirements.txt
├── scaler.pkl
├── train_model.py
│
└── README.md

Disclaimer

This system is developed for educational and research purposes only. Prediction results generated by the system are not considered medical diagnoses and should not replace professional medical consultation.