Spaces:
Sleeping
Sleeping
| title: CKD Prediction System | |
| emoji: 🫘 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.23.3 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Chronic Kidney Disease (CKD) Prediction System | |
| **Research Project | PAF-IAST | HEC-Funded** | |
| **Principal Investigator:** Dr. Muhammad Rizwan | |
| --- | |
| ## Overview | |
| This web application provides an AI-powered screening tool for **Chronic Kidney Disease (CKD)** | |
| based on routine blood test parameters. It was developed as part of a peer-reviewed research study | |
| conducted at the **Pakistan Air Force Institute of Aerospace Technology (PAF-IAST)** under an | |
| HEC-funded project. | |
| The application accepts 21 blood parameters as input and returns a binary prediction | |
| (**CKD / No CKD**) along with class probabilities. | |
| --- | |
| ## Model | |
| | Property | Details | | |
| |---|---| | |
| | Algorithm | Random Forest Classifier | | |
| | Accuracy | ~95.5% | | |
| | F1 Score | ~95.4% | | |
| | ROC-AUC | ~98.5% | | |
| | Validation | 5-fold Stratified Cross-Validation | | |
| | Explainability | SHAP + LIME | | |
| The Random Forest model was selected after benchmarking 8 classifiers including | |
| XGBoost, CatBoost, Gradient Boosting, AdaBoost, Logistic Regression, Decision Tree, | |
| and K-Nearest Neighbors. | |
| --- | |
| ## Input Features | |
| The model uses the following 21 blood test parameters: | |
| **Demographic:** Sex, Age | |
| **CBC:** Haemoglobin, Hematocrit, RBC Count, WBC Count, Platelet Count | |
| **Red Cell Indices:** MCH, MCHC, MCV, RDW-SD, MPV, PCT, PDW | |
| **Differential Count:** Lymphocytes, Monocytes, Polymorphs, Eosinophils | |
| **Kidney Function:** Creatinine, Blood Urea, GFR | |
| --- | |
| ## Key Findings (from SHAP Analysis) | |
| Top predictive features (in order of importance): | |
| 1. **Creatinine** — strongest CKD indicator | |
| 2. **GFR** (Glomerular Filtration Rate) — inverse relationship with CKD | |
| 3. **Urea** — elevated in kidney dysfunction | |
| 4. **RDW-SD** — red cell distribution width | |
| 5. **MPV** — mean platelet volume | |
| --- | |
| ## Dataset | |
| - **Samples:** 398 patients | |
| - **Class distribution:** CKD: 241 (60.6%) | No CKD: 157 (39.4%) | |
| - **Missing values:** < 1.5% (imputed with zero-fill) | |
| - **No duplicates** | |
| --- | |
| ## Files in this Space | |
| ``` | |
| app.py ← Gradio application | |
| requirements.txt ← Python dependencies | |
| Random_Forest.pth ← Trained Random Forest model (joblib format) | |
| README.md ← This file | |
| ``` | |
| --- | |
| ## Disclaimer | |
| > This application is intended **for research and educational purposes only**. | |
| > It does **not** constitute medical advice, diagnosis, or treatment. | |
| > All clinical decisions must be made by a licensed and qualified physician. | |
| --- | |
| ## Citation | |
| If you use this work, please cite the associated research paper (details to be added upon publication). | |
| **Contact:** Dr. Muhammad Rizwan | PAF-IAST |