You are a data scientist working for a healthcare company that develops predictive models for diagnosing diseases based on patient data. Due to regulatory requirements and the critical nature of healthcare decisions, model interpretability is a top priority. The company needs to ensure that the predictions made by the model can be explained to both medical professionals and regulatory bodies. You are evaluating different algorithms in Amazon SageMaker for your model, balancing the trade-off between accuracy and interpretability. The initial trials show that more complex models like deep neural networks (DNNs) yield higher accuracy but are less interpretable, whereas simpler models like logistic regression provide clearer insights but may not perform as well on the dataset. Given these considerations, which of the following approaches is MOST APPROPRIATE for achieving both interpretability and acceptable performance?