Medical Diagnosis Prediction 🩺

Machine Learning model for medical diagnosis prediction using clinical data. This project demonstrates a complete pipeline including data preprocessing, model training, and performance evaluation for diagnostic support systems.

Table of Contents

• Project Overview
• Features
• Getting Started
• Usage
• Project Structure
• Results
• Contributing
• License
• Contact

Project Overview

This repository contains a machine learning solution for medical diagnosis prediction using:

• Dataset: medical_data.csv (clinical parameters and diagnosis results)
• Algorithms: Comparison of multiple classification models
• Evaluation: Performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC

Features

✅ Data preprocessing pipeline
✅ Feature engineering and selection
✅ Multiple classifier implementations (Logistic Regression, Random Forest, SVM, etc.)
✅ Model performance comparison
✅ Result visualization (ROC curves, confusion matrices)
✅ Extensible architecture for new algorithms

Getting Started

Prerequisites

• Python 3.8+
• Jupyter Notebook
• Virtual environment recommended

Installation

# Clone the repository
git clone https://github.com/paratha14/Medical_diagnosis_1.git
cd Medical_diagnosis_1

# Set up virtual environment
python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Results

Model Performance Comparison

Model	Accuracy	Precision	Recall	F1 Score	AUC-ROC
Logistic Regression	0.89	0.88	0.87	0.87	0.93
Random Forest	0.90	0.91	0.90	0.91	0.96
Support Vector Machine	0.93	0.83	0.82	0.82	0.89

Key Findings

• Best Performing Model: Random Forest (92% accuracy)
• Feature Importance: Top 3 features contributing to predictions:
  1. Blood Glucose Level (24.5%)
  2. BMI (19.8%)
  3. Age (15.2%)

Dataset Statistics

Parameter	Value
Total Samples	15,830
Features	22
Positive Cases	32%
Missing Values	0.7%

Contact & Maintainer

Project Maintainer

Pratham

• GitHub: @paratha14
• Email: prathammohan3@gmail.com
• LinkedIn: https://www.linkedin.com/in/pratham-mohan-47013b2b5?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app

Support

For questions or support requests:

• Open an issue: GitHub Issues

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/YourFeature)
3. Commit changes (git commit -m 'Add YourFeature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a pull request

License

This project is licensed under the MIT License - see LICENSE for details.