Example considered :School oral health promotion:
Data to Collect:
- Demographics: Age, gender, socioeconomic status.
- Oral Health Practices: Frequency of brushing, use of fluoridated toothpaste, flossing habits.
- Dietary Patterns: Frequency of sugary snack and drink consumption.
- Oral Health Outcomes: DMFT (Decayed, Missing, Filled Teeth) index scores or plaque levels.
- Parental Involvement: Awareness and encouragement of oral health practices.
Kasuni Kalubowila
For K-means cluster analysis, you need numerical data with multiple attributes that represent distinct features of your observations. The data should be standardized or normalized if the variables have different scales, and missing values must be addressed. While K-means works best with continuous data, categorical variables can be encoded numerically or handled with different clustering
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