Building an Anemia Detection System Using Machine Learning 🚑

Machine Learning in Anemia Detection: A Force for Healthcare 🚑

In healthcare, powerful allies we need - and machine learning has proven to be one of the strongest. I recently developed a system that predicts anemia with 99% accuracy using Random Forest Classification. Let me share this journey with you.

The Challenge at Hand 🎯

Anemia affects millions worldwide, and traditional detection methods take considerable time. Our mission was clear:

• Quick assessment using blood test parameters
• Accurate predictions through machine learning
• Accessible interface for all healthcare professionals

Our Technical Arsenal 🛠️

For this quest, we chose powerful tools:

• Python for core implementation
• scikit-learn for machine learning capabilities
• Flask for web application
• SMOTE for handling data imbalance

The Machine Learning Path 🧪

Data Preparation

Five key parameters, measure we must:

• Hemoglobin Levels
• Mean Corpuscular Volume (MCV)
• Mean Corpuscular Hemoglobin (MCH)
• Mean Corpuscular Hemoglobin Concentration (MCHC)
• Gender

Balancing the Data

Imbalanced data, our greatest challenge it was. Using SMOTE, we achieved perfect balance: 801 samples per class, critical for unbiased training.

Model Performance 📊

Algorithm	Accuracy	AUC
Random Forest	99%	99%
Logistic Regression	98%	98%
SVM	90%	90%
KNN	87%	87%

The Random Forest algorithm emerged victorious, showing exceptional performance across all metrics.

Feature Importance: Understanding Our Strength 🔍

Our analysis revealed:

• Hemoglobin: The strongest predictor (83.9%)
• Gender: A significant factor (9.1%)
• MCH: Contributing 2.7% to accuracy

The Web Application: Knowledge Shared 💻

🌟 Experience it here: Anemia Detection Web App

📂 Explore the code: GitHub Repository

Future Developments 🔮

The journey continues with plans for:

• Expanding our dataset
• Implementing XGBoost models
• Cloud platform deployment

Join Our Community 🤝

Together, stronger we become. Contribute through:

• Testing and feedback
• Feature suggestions
• Code contributions

This project remains open-source under the MIT License.

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Have you explored similar healthcare projects? Share your experiences below! 💭

MachineLearning #Healthcare #DataScience #Python #OpenSource