Machine Learning Predictive Diagnostic Risk Model

Develop a comprehensive Python-based predictive diagnostic risk model using scikit-learn and TensorFlow that can assess cardiovascular disease probability based on complex multi-dimensional patient data. The model must integrate diverse data sources including genetic markers, historical medical records, lifestyle factors, and current health metrics. Implement k-fold cross-validation, handle class imbalance, and generate interpretable risk probability scores with confidence intervals. The solution should be deployable as a Flask microservice with clear model explainability features.