You are a data scientist at a healthcare company developing predictive models for disease diagnosis based on patient data. Due to regulatory requirements and the critical nature of healthcare decisions, model interpretability is a top priority. The company must ensure that model predictions can be explained to medical professionals and regulatory bodies. Initial trials show that complex models like deep neural networks (DNNs) yield higher accuracy but are less interpretable, while simpler models like logistic regression offer clearer insights but lower performance. Considering these factors, which approach is MOST APPROPRIATE for achieving both interpretability and acceptable performance?