COVID-19 Diagnosis Prediction Using Machine Learning

📌 Overview

This project leverages Machine Learning to predict COVID-19 infection outcomes based on patient symptoms and demographic data. The goal is to enhance early detection, optimize resource allocation, and improve healthcare efficiency.

🚀 Features

• Predicts COVID-19 infection using Logistic Regression, Decision Trees, Random Forest, and XGBoost
• Data preprocessing includes EDA, feature selection, and hyperparameter tuning
• Implements train-test splitting for robust model evaluation
• Provides insights into key symptoms affecting diagnosis
• Scalable approach for future disease prediction models

📂 Project Structure

├── data/                # Dataset used for training/testing
├── models/              # Trained machine learning models
├── notebooks/           # Jupyter notebooks with analysis & visualization
├── scripts/             # Python scripts for data processing & training
├── README.md            # Project documentation (this file)
├── requirements.txt     # Dependencies and libraries

🛠️ Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn, XGBoost
• Data Storage: MySQL (for structured patient records)
• Model Evaluation: Accuracy, Precision, Recall

📊 Model Performance

Model	Accuracy
Logistic Regression	93.14%
Decision Tree	94.46%
Random Forest	94.46%
XGBoost Classifier	94.46%

🔬 Methodology

1. Data Collection: Patient symptom and demographic data
2. EDA & Feature Engineering: Data cleaning, correlation analysis, feature selection
3. Model Training: Multiple ML models trained & tuned for performance
4. Evaluation: Comparison of models using various performance metrics
5. Deployment Considerations: Scalability for predicting other diseases

📌 Key Insights

• Cough, Fever, and Shortness of Breath are strong indicators of COVID-19.
• Decision Tree, Random Forest, and XGBoost showed similar accuracy (94.46%).
• Feature scaling & encoding techniques improved model performance.
• Potential for future applications in detecting other infectious diseases.

🔗 References

• Johns Hopkins University COVID-19 Dataset
• UCI Machine Learning Repository
• Research Papers on ML-based COVID-19 Diagnosis

📥 Installation & Usage

# Clone the repository
git clone https://github.com/your-username/covid19-ml-prediction.git
cd covid19-ml-prediction

# Install dependencies
pip install -r requirements.txt

# Run model training
python scripts/train_model.py

📢 Contributing

Contributions are welcome! Feel free to fork the repo, create a feature branch, and submit a PR.

📜 License

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

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