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MHA 705 Exam 5 questions and answers

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MHA 705 Exam 5 questions and answers

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  • September 21, 2024
  • 5
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • MHA 705
  • MHA 705
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lizmwikali72
MHA 705 Exam 5
Data Mining focusing on producing a solution that generates useful forecasting through a four
phase process
1. Problem identification
2. Exploration of the data
3. Pattern discovery
4. Knowledge deployment—application to new data to forecast or generate predictions


Data Mining facilitates
data exploration;

-Resulting knowledge discovery fosters proactive, knowledge driven decision making


Exploratory Data Analysis (EDA)
Pattern discovery is a complex phase of data mining.
-Yields a highly predictive, consistent pattern identifying model
-Sometimes known as model building or pattern identification


Data Mining Known as KDD
- Knowledge discovery and data mining
- Knowledge discovery and data
- Knowledge discovery in databases


KDD
- Term "knowledge discovery" is key
- Data mining looks at the data from different vantage points, aspects, and perspectives
- Brings new insights to the data set


Data Mining Defined
Process of finding correlations or patterns among the data


Data Mining is considered ______ data analysis because:
secondary; data miners use databases created by others, often for purposes unrelated to
research and data mining.

, KDD and Research
Healthcare researchers are positioned to use data mining technologies to transform the
repositories of big data into comprehensible knowledge that is useful for guiding their practice
and facilitating interdisciplinary research.


Classification and Regression Trees (CART)
Data mining method for analyzing outcomes and service use


Data Mining Concepts
- Bagging
- Boosting
- Data reduction
- Drill down
- EDA
- Feature selection
- Machine learning
- Meta-learning
- Predictive
- Stacking


Data Mining Techniques
- Neural networks
- Decision trees
- Chi-square automatic interaction detection (CHAID)
- Rule induction
- Algorithm
- Nearest neighbor
- Text mining
- Online analytic processing (OLAP)
- Brushing


Data Mining Models
CRISP-DM, Six Sigma, SEMMA


CRISP-DM
Six steps: business understanding, data understanding, data preparation, modeling, evaluation,
and deployment

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