Maternal Mental Health Data Analysis - 01/07/2026 13:08 EDT
Budget: $15 – $25 USD
Project Description
I am looking for a data science or public health research expert to analyze maternal mental health conditions and identify key risk factors affecting mothers’ psychological well-being.
The objective is to develop a data-driven understanding of maternal mental health status and potentially build predictive models to identify mothers at risk of depression, anxiety, or related mental health conditions.
The study is intended to support evidence-based interventions in maternal and child health programs.
Key Requirements
Data cleaning and preprocessing of maternal mental health datasets
Descriptive statistical analysis of mental health indicators among mothers
Identification of socio-demographic, economic, and health-related risk factors
Application of statistical modeling (e.g., logistic regression or multilevel models)
Optional machine learning models for risk prediction (e.g., Random Forest, XGBoost)
Interpretation of results in a public health context
Deliverables
Cleaned and analyzed dataset
Statistical analysis report with interpretation
Predictive model (if applicable)
Visualizations of key findings
Well-documented methodology and results
Ideal Candidate Should Have
Strong background in statistics, biostatistics, or data science
Experience with public health or mental health research
Knowledge of regression analysis and machine learning
Proficiency in Python, R, SPSS, or STATA
Ability to interpret results in a health research context
Additional Note
Experience in maternal health or mental health research will be highly valued. Please include examples of similar analytical or research projects in your proposal.
I am looking for a data science or public health research expert to analyze maternal mental health conditions and identify key risk factors affecting mothers’ psychological well-being.
The objective is to develop a data-driven understanding of maternal mental health status and potentially build predictive models to identify mothers at risk of depression, anxiety, or related mental health conditions.
The study is intended to support evidence-based interventions in maternal and child health programs.
Key Requirements
Data cleaning and preprocessing of maternal mental health datasets
Descriptive statistical analysis of mental health indicators among mothers
Identification of socio-demographic, economic, and health-related risk factors
Application of statistical modeling (e.g., logistic regression or multilevel models)
Optional machine learning models for risk prediction (e.g., Random Forest, XGBoost)
Interpretation of results in a public health context
Deliverables
Cleaned and analyzed dataset
Statistical analysis report with interpretation
Predictive model (if applicable)
Visualizations of key findings
Well-documented methodology and results
Ideal Candidate Should Have
Strong background in statistics, biostatistics, or data science
Experience with public health or mental health research
Knowledge of regression analysis and machine learning
Proficiency in Python, R, SPSS, or STATA
Ability to interpret results in a health research context
Additional Note
Experience in maternal health or mental health research will be highly valued. Please include examples of similar analytical or research projects in your proposal.