Child Mortality Risk Factor Dataset

Title: Child Mortality Risk Factor Dataset

Description: A dataset containing sociodemographic, maternal, and child health variables to predict child mortality (binary classification: 0 = survived, 1 = died).

Key Variables:

Demographics: Mother’s age, father’s age, residence (urban/rural).

Maternal/Child Health: Birth order, birth weight (kg), antenatal visits, institutional delivery (yes/no), vaccination status (yes/no), low birth interval (yes/no).

Socioeconomic Factors: Mother’s education level (no/primary/secondary/higher), wealth index (poor/middle/rich), access to water (yes/no), toilet facility (yes/no).

Target Variable: Mortality (0 or 1).B. Variable Descriptions
Add a table or list explaining each column:

Variable Type Description
mother_age Numerical Age of the mother in years.
father_age Numerical Age of the father in years.
birth_order Numerical Birth order of the child (e.g., 1 = firstborn).
birth_weight Numerical Birth weight in kilograms (kg).
mother_education Categorical Education level: No/Primary/Secondary/Higher.
wealth_index Categorical Socioeconomic status: Poor/Middle/Rich.
residence Categorical Urban or rural residence.
antenatal_visits Numerical Number of antenatal care visits during pregnancy.
institutional_delivery Binary Whether delivery occurred in a healthcare facility (Yes/No).
vaccination_status Binary Whether the child received vaccinations (Yes/No).
access_to_water Binary Access to clean water (Yes/No).
toilet_facility Binary Access to improved sanitation (Yes/No).
low_birth_interval Binary Short interval between pregnancies (Yes/No).
mortality Binary Target variable: 0 = survived, 1 = died.
Purpose: Predict child mortality risk using machine learning (e.g., logistic regression, decision trees, neural networks).

Keywords: Child mortality, predictive modeling, socioeconomic factors, maternal health, machine learning