The primary ethical concern in this use case is "Data ownership and consent." When using data from various sources, including social media, it's essential to consider data ownership, consent, and privacy rights. Proper consent and data protection measures are critical to ensure ethical practices in healthcare data analysis.

The ROC (Receiver Operating Characteristic) Curve plots the True Positive Rate (Sensitivity) against the False Positive Rate for different threshold values of a classification model. It is used to evaluate the model's performance in distinguishing between classes at various thresholds.

To ensure that both 'Age' and 'Blood Pressure' contribute equally to the distance metric in a k-NN algorithm, you should standardize both features. Standardization scales the features to have a mean of 0 and a standard deviation of 1, preventing one from dominating the distance calculation. Normalization may not achieve this balance, and changing the distance metric or giving more weight to one feature can bias the results.

Seaborn is a popular data visualization library in Python that provides a heatmap function, commonly used to visualize correlation matrices. Heatmaps are effective for displaying the correlation between variables, making it easier to identify relationships in complex datasets.

Apache Spark provides in-memory processing capabilities, allowing for faster analytics compared to traditional batch processing. It's an excellent choice when speed and real-time data processing are priorities.

Data Analysts primarily focus on generating insights from data. They use statistical and analytical techniques to draw meaningful conclusions and communicate their findings to support decision-making.

In a confusion matrix, the value representing correctly predicted positive instances is called "True Positive." This refers to the cases where the model correctly identified positive instances in the dataset. Understanding True Positives is essential for assessing the model's performance, especially in classification tasks.

Power BI is a data visualization tool that enables users to create interactive dashboards and stories to gain better business insights. It offers a wide range of features for data analysis, visualization, and reporting, making it a popular choice for business intelligence.

Data Scientists are well-suited for tasks like understanding sentiment through NLP. They have the skills to leverage machine learning and NLP techniques to extract insights from text data. They can develop models to analyze product reviews and assess sentiment.

In transfer learning, fine-tuning is the process of updating the weights of a pre-trained model with new data. This allows the model to adapt to the specific characteristics of the new data while leveraging the knowledge learned from the pre-training on a different but related task.