1. Define data mining in the context of advancing

healthcare. Why is data mining considered crucial for

improving patient care? (Answer: Data mining is the

process of extracting patterns and actionable insights from

large sets of electronic health records or other healthcare

databases. It is essential for advancing healthcare as it

allows for identifying trends, predicting outcomes, and

personalizing treatment plans based on evidence-based

medicine.)

2. Explain the concept of predictive modeling in healthcare

data mining. How can predictive models help healthcare

professionals make informed decisions? (Answer:

Predictive modeling involves using historical data to build

a model that can predict future outcomes or events. In

healthcare, these models can help clinicians identify

patients at risk of specific conditions or adverse events,

enabling early interventions and targeted care.)

3. Discuss the role of data cleansing and preprocessing in

health data mining. Why is it critical to ensure the accuracy

and quality of the data before mining? (Answer: Data

cleansing and preprocessing involves removing irrelevant

or inaccurate data, standardizing formats, and handling

missing values. Ensuring data accuracy is paramount in

healthcare as incorrect or incomplete data can lead to

erroneous conclusions and potentially compromise patient

safety.)