Skin Cancer Risk Prediction
Machine Learning for Clinical Risk Classification
Predicting skin cancer risk using interpretable machine learning models
This project explored how demographic, clinical, and behavioral factors can be combined to estimate skin cancer risk. I compared regularized logistic regression models and evaluated their ability to balance predictive performance with clinical interpretability.
Role
Machine Learning Researcher
Domain
Healthcare Analytics
Stack
R · glmnet · Logistic Regression
Techniques
LASSO · Ridge · Cross Validation
Project Snapshot
Problem
Early identification of individuals at higher risk for skin cancer can improve screening strategies and preventative care.
Solution
Built and evaluated regularized logistic regression models to identify important predictors while reducing overfitting.
Outcome
Compared LASSO and Ridge models using cross-validation, feature selection, calibration analysis, and predictive performance metrics.
Overview
Skin cancer is one of the most common forms of cancer worldwide, yet many of its strongest risk factors are already available through demographic, lifestyle, and clinical information.
The goal of this project was to build interpretable predictive models capable of estimating an individual’s likelihood of developing skin cancer while understanding which variables contributed most strongly to prediction.
The Challenge
High-dimensional predictors
Clinical datasets often contain many correlated variables that can lead to unstable models.
Preventing overfitting
The model needed to generalize well to unseen patients rather than memorizing the training data.
Model interpretability
Healthcare applications require models that clinicians can understand and trust.
Performance evaluation
Beyond prediction accuracy, calibration and feature stability were important for evaluating model quality.
Modeling Pipeline
The project followed a structured supervised learning workflow from data preparation through model evaluation.
Data Preparation
Cleaned demographic, behavioral, and clinical variables.
Feature Engineering
Processed predictors and standardized variables.
Model Training
Fit logistic regression with LASSO and Ridge regularization.
Cross Validation
Selected optimal regularization parameters.
Model Evaluation
Compared predictive performance and calibration.
Interpretation
Identified the most influential predictors and assessed clinical usefulness.
End-to-end machine learning workflow for clinical risk prediction.
Technical Stack
- R
- glmnet
- Logistic Regression
- LASSO
- Ridge Regression
- Cross Validation
- ROC Analysis
- Calibration
- Feature Selection
What I Built
Predictive Models
Built multiple regularized logistic regression models to predict skin cancer risk while balancing model complexity and interpretability.
Hyperparameter Optimization
Used cross-validation to identify optimal penalty parameters for both LASSO and Ridge regression.
Model Comparison
Compared predictive performance, calibration, and feature selection across competing models.
Clinical Interpretation
Analyzed selected variables to better understand which patient characteristics contributed most strongly to predicted risk.
Key Results
Regularized models compared
Cross-validation used for model selection
Interpretable classification framework
What I Learned
This project reinforced that predictive performance alone is not enough in healthcare applications. Regularization techniques such as LASSO and Ridge help improve generalization while producing models that remain interpretable for clinical decision making.
I also gained experience evaluating machine learning models beyond simple accuracy by incorporating cross-validation, calibration, and feature selection into the modeling process.
Full Technical Report
The complete technical report, including methodology, statistical analysis, and model evaluation, is available below.