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In the development of a machine learning model for a healthcare application predicting patient readmission risks, the team has completed data collection, feature engineering, model training, and hyperparameter tuning. The application must comply with HIPAA regulations, ensure high accuracy to avoid misclassification risks, and be scalable to handle increasing patient data volumes. Which step is critical to conclude the model development process, ensuring the model's readiness for deployment by rigorously assessing its performance against a separate test dataset under these constraints? Choose the best option.
A
Feature engineering, where the model's input features are selected and transformed to improve model performance, considering the sensitivity of healthcare data.
B
Data collection, the initial phase where raw patient data is gathered and anonymized to comply with HIPAA regulations.
C
Hyperparameter tuning, a critical step in optimizing the model's parameters for better accuracy, especially important given the high stakes of healthcare predictions.
D
Model evaluation, the phase where the model's performance is thoroughly assessed on unseen patient data to ensure its effectiveness, compliance, and readiness for deployment under the given constraints.
E
Both A and C are necessary final steps to ensure the model's readiness for deployment in a healthcare setting.