RESEARCH OVERVIEW
From Data to Diet:
Machine Learning Approaches for Predicting Household Nutrition Outcomes in Bangladesh
Nutrition is closely connected to a family’s economic condition.
CORE HOUSEHOLD PILLARS
PHYSIOLOGICAL VITALITY
Health
Physical well-being, growth, and disease resistance
HUMAN CAPITAL
Education
Nutritional awareness, literacy, and dietary diversity
ECONOMIC CAPACITY
Ability to Work
Productive labor capacity, earnings, and family income
Computer Science / Machine Learning Presentation
ARM Abir Hasan
MULTIDIMENSIONAL DETERMINANTS
WHAT AFFECTS A FAMILY'S NUTRITION?
INCOME
FOOD PRICES
FAMILY SIZE
TARGET OUTCOME
HOUSEHOLD NUTRITION
Vulnerability & Intake Quality
EDUCATION
EMPLOYMENT
LOCATION
Can we use household data and machine learning to predict which families are more likely to face food insecurity?
METHODOLOGICAL PIPELINE
FROM DATA → PREDICTION
HIES
Household Income & Expenditure Survey
BDHS
Bangladesh Demographic & Health Survey
01
HOUSEHOLD DATA
Income
Food expenditure
Family size
Education
Employment
Children
Rural / Urban
NEURAL ENGINE
MACHINE LEARNING
Logistic Regression
Decision Tree
Random Forest
Pattern recognition across multi-dimensional socio-economic vectors
02
PREDICTION
Food Secure
Sufficient nutritional access & stability
Moderately Insecure
Compromised diet quality & frequency
Severely Insecure
High vulnerability; immediate support needed
IMPACT & ETHICAL CONSIDERATIONS
WHY DOES IT MATTER?
TARGETED ASSISTANCE
LIMITED RESOURCES
IDENTIFY VULNERABLE HOUSEHOLDS
need support, such as food assistance and nutrition programs
CORE SYSTEM DYNAMICS
THE POVERTY–NUTRITION CYCLE
BREAK THE
CYCLE
LOW INCOME
POOR NUTRITION
POOR HEALTH
LOWER PRODUCTIVITY
CRITICAL CONSTRAINTS
CAUTION
BUT ML ISN'T PERFECT
Poor-quality data
Incomplete surveys & missing attributes
Bias
Underrepresented remote rural cohorts
Privacy
Protecting household identifiable records
Prediction ≠ Causation
Models correlate; systemic policy solves causes
SUMMARY & CONCLUSION
“So, overall, machine learning can help us understand nutrition problems and help the government make better decisions.”
THANK YOU
ARM Abir Hasan
Computer Science & Machine Learning