Machine Learning Predictive Risk Assessment Model

Develop a scikit-learn and TensorFlow-based predictive model that can assess cardiovascular disease risk using comprehensive patient health data. The model must incorporate feature engineering techniques for medical datasets, handle multiple data types (categorical, continuous), implement cross-validation with stratified sampling, and provide interpretable risk scores with confidence intervals. Include a comprehensive model evaluation framework that compares performance across different machine learning algorithms.