Simulated pediatric growth and development assessment dataset with growth measurements, developmental scores, and demographic factors. Designed to test pediatric-specific decision trees, growth delay prediction, and developmental assessment models.
Format
A data frame with 200 children and 12 variables:
- child_id
Character. Unique child identifier (PED_0001 to PED_0200)
- height_cm
Numeric. Height measurement (cm)
- weight_kg
Numeric. Weight measurement (kg)
- head_circumference
Numeric. Head circumference (cm)
- age_months
Integer. Age in months
- motor_score
Integer. Motor development score
- cognitive_score
Integer. Cognitive development score
- sex
Factor. Child sex ("Male", "Female")
- birth_weight
Factor. Birth weight category ("Normal", "Low", "Very Low")
- gestational_age
Factor. Gestational age ("Term", "Preterm")
- growth_delay
Factor. Primary outcome - growth delay status ("Normal", "Delayed")
- study_site
Factor. Study site ("site_A", "site_B")
- maternal_age
Integer. Maternal age at birth (years)
- socioeconomic_status
Factor. Socioeconomic status ("Low", "Medium", "High")
- x_coord, y_coord
Numeric. Geographic coordinates for population analysis
Details
This dataset simulates a comprehensive pediatric growth and development study with anthropometric measurements, developmental assessments, and demographic factors. The dataset reflects realistic patterns of pediatric growth and development outcomes.
Clinical Context:
Pediatric growth monitoring
Developmental delay screening
Early childhood assessment
Population health surveillance
Key Features:
Age-appropriate growth measurements
Developmental assessment scores
Birth history and demographic factors
Socioeconomic considerations
Multi-site study design
Geographic variation modeling
Recommended Analysis Scenarios:
Growth delay prediction models
Developmental screening tools
Multi-site validation studies
Socioeconomic impact analysis
Age-stratified decision trees
Missing data handling in pediatric studies
Examples
if (FALSE) { # \dontrun{
# Load the dataset
data(pediatric_growth)
# Growth delay prediction
result <- tree(
data = pediatric_growth,
vars = c("height_cm", "weight_kg", "head_circumference", "age_months"),
facs = c("sex", "birth_weight", "gestational_age"),
target = "growth_delay",
targetLevel = "Delayed",
train = "study_site",
trainLevel = "site_A",
clinicalContext = "screening",
imputeMissing = TRUE,
showInterpretation = TRUE
)
} # }