OvaPredict: PCOS Risk Analysis & Prediction

OvaPredict is a machine learning-based web application designed to predict the risk of Polycystic Ovary Syndrome (PCOS) using clinical and lifestyle-related health parameters.

Features

• PCOS risk prediction using Machine Learning
• Random Forest Classifier with ~91% accuracy
• Interactive Streamlit web interface
• Data preprocessing and SMOTE balancing
• Multiple model comparison:
  • Logistic Regression
  • Linear SVM
  • Decision Tree
  • Random Forest

Tech Stack

• Python
• Pandas
• Scikit-learn
• Streamlit
• Joblib

ML Workflow

1. Data Cleaning
2. Exploratory Data Analysis (EDA)
3. Handling Missing Values
4. SMOTE for class balancing
5. Model Training & Evaluation
6. Model Saving using .pkl

Model Performance

Model	Accuracy	Precision	Recall	F1 Score	ROC-AUC
Logistic Regression	0.8257	0.6977	0.8333	0.7595	0.9159
Linear SVM	0.8716	0.7619	0.8889	0.8205	N/A
Random Forest	0.9083	0.8824	0.8333	0.8571	0.9140
Decision Tree	0.8532	0.7941	0.7500	0.7714	0.8834

Best Model

Random Forest Classifier

• Accuracy: 90.8%
• F1 Score: 0.8571
• ROC-AUC: 0.9140

Run the App

Install dependencies:

pip install -r requirements.txt