,1. Which of the following algorithms is most suitable for
predicting patient outcomes based on historical health
data?
a) K-means Clustering
b) Decision Trees
c) Naive Bayes
d) Apriori
Answer: b) Decision Trees
Rationale: Decision Trees are ideal for predictive
modeling and are suited for categorical and continuous
data, making them effective for predicting patient outcomes.
2. In the context of nursing informatics, which metric is
commonly used to evaluate the performance of a
classification model?
a) R-squared
b) Mean Absolute Error
c) F1 Score
d) Root Mean Square Error
Answer: c) F1 Score
Rationale: The F1 score is a balance between precision
and recall and is critical in healthcare for evaluating
classification models, especially when class distribution is
uneven.
3. Which data visualization technique is best used to show
the distribution of patient demographics like age?
, a) Line Chart
b) Bar Chart
c) Histogram
d) Pie Chart
Answer: c) Histogram
Rationale: Histograms are used to show the distribution
of a dataset and are perfect for viewing the spread and
pattern of continuous data such as age.
### Fill-in-the-Blank Questions
4. ___________ is a method in data analytics used for
detecting patterns and causal relationships in healthcare
data.
Answer: Association Rule Mining
Rationale: Association rule mining is used for uncovering
interesting relations between variables in large databases,
useful for finding patterns in healthcare data.
5. In data science, __________ is the process of adjusting
data to reduce redundancy and improve data integrity,
particularly critical in managing patient information.
Answer: Data Normalization
Rationale: Data normalization reformats data to reduce
redundancy and improve data integrity, crucial in managing
patient records in healthcare.
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