Kidney-Disease-Classification

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🔄 Workflow

1. Data Collection
2. Data Cleaning & Preprocessing
3. Feature Selection / Image Processing
4. Model Building
5. Model Training
6. Evaluation & Validation
7. Prediction & Visualization

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📈 Model Evaluation

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix
• ROC-AUC Curve

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🚀 How to Run the Project

1. Clone the repository:```bash git clone https://github.com/your-username/kidney-disease-classification.git
2. Install dependencies: pip intall -r requirements.txt
3. Run Jupyter Notebook or Python Script
4. Train the model and Evaluate

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📈 Results & Performance

The proposed Deep Learning–based Kidney Disease Classification model demonstrated excellent performance on the test dataset, indicating its strong ability to distinguish between healthy and diseased cases.

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✅ Key Highlights:

The model achieved high classification accuracy, showing reliable prediction capability Low false-negative rate, which is critical in medical diagnosis systems Consistent performance across training and validation datasets, indicating minimal overfitting Clear separation between classes observed in evaluation metrics

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🧪 Evaluation Metrics Used:

Accuracy, Precision, Recall, F1-Score, Confusion Matrix, ROC–AUC Curve

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📊 Performance Summary:

The confusion matrix shows a high true positive and true negative rate Precision and recall values indicate balanced and trustworthy predictions ROC–AUC curve demonstrates strong discriminative power of the model

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📌 Outcome:

The results confirm that the deep learning model is effective for early-stage kidney disease classification and can be used as a decision-support system in healthcare analytics (for research and healthcare purposes).