🧬 Kidney Stone Risk Prediction
Clinical Machine Learning for Early Kidney Stone Risk Stratification
🩺 Clinical Motivation
Kidney stone disease affects millions of patients worldwide and is associated with:
- recurrent emergency visits
- severe pain episodes
- long-term renal complications
- high healthcare costs
Early identification of patients at higher risk enables:
- preventive interventions
- lifestyle and dietary recommendations
- optimized follow-up
- reduced recurrence and complications
This project develops a machine learning model to support clinicians in early risk stratification using routinely collected clinical variables.
📊 Project Overview
This work includes:
- structured clinical data preprocessing
- exploratory data analysis
- clinically guided feature engineering
- model development and evaluation
- interpretability and clinical insight generation
The goal is to build a transparent, reproducible, and clinically meaningful model that can support decision-making in real-world settings.
🧪 Dataset
The dataset includes:
- demographic variables
- laboratory results
- clinical history
- other relevant clinical features
Data preprocessing steps include:
- handling missing values
- detecting and managing outliers
- encoding categorical variables
- scaling or transforming variables when appropriate
🤖 Modeling Approach
A supervised learning approach is used to predict the risk of renal lithiasis based on clinical features.
Several algorithms were evaluated:
- Logistic Regression
- Random Forest
The final selection was based on:
- discrimination (ROC-AUC)
- calibration
- interpretability
- clinical plausibility
Cross-validation was performed to ensure robustness and generalizability.
Key metrics evaluated:
- ROC-AUC
- Precision–Recall
- Sensitivity / Specificity trade-offs
- Calibration curves
The model demonstrates strong predictive performance and clinically coherent behavior across subgroups.
📈 Evaluation
The model is evaluated using:
- discrimination metrics
- calibration assessment
- clinically relevant thresholds and trade‑offs
Emphasis is placed on:
- robustness
- generalizability
- interpretability for clinicians
🔍 Interpretability
To ensure clinical trust and transparency, the project includes:
- feature importance analysis
- partial dependence plots
These tools help clinicians understand why the model makes certain predictions.
⚠️ Limitations
- Single-center dataset
- Potential selection bias
- Limited imaging variables
- Requires external validation before deployment
📁 Project Structure
- README.md — Executive and technical summary
- notebooks/ — EDA, modeling, interpretability workflows
- src/ — Modular, reproducible code
- reports/ — Technical and clinical documentation
- requirements.txt — Environment reproducibility
🔗 Useful Links
Last updated: January 2026