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Our team dives into Indiana's maternal health data to analyze the factors impacting infant mortality rates using statistical tools and models. Insights reveal the influence of variables such as low birth weight, breastfeeding, complications upon birth, hospital transfers, and marital status. Key impactful variables include the number of infant insurance claims, maternal diabetes prescriptions, and median household income per county.
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Analyzing Indiana data to help reduce Hoosier infant mortality Morgan Hogenmiller, Marzieh Mirzaei, and Brad Brechbuhl
Our team’s mission: Dive into Indiana's maternal health data to analyze what impacts infant mortality rate using statistical tools and models
Data Used Linear and logistic regression Chi-squared Test
Multiple Linear Regression Model Insights Infant deaths = 65.74 + 7.5 * Median Household Income 19.12 * Total Diabetes Prescriptions 12.98 * Total Infant Insurance Claims Diagnostics Adjusted R-squared: .76 RMSE: 5.56
Classification - Logistic regression Logostic regression Model classified output Selected features Threshold selected from a certain number of SD identified as an outlier Classified the output Class1: High rate Infant mortality rate Class1: Low rate Threshold : 0.0005
Logistic Regression -- Accuracy Test dataset fed to the logistic regression model Considered different thresholds (T) Per Threshold (T) True positive rate (TPR) & False positive rate(FPR) Area under the curve (AUC) Accuracy: 0.65
Key Insights • We now know that low birth weight, breastfeeding, complications upon birth, hospital transfers, and marital status may influence infant mortality • The three most impactful variables based on our feature and model analyses were: • Number of Infant insurance claims per county • Number of maternal diabetes prescriptions per county • Median household income per county
