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What is Child Mortality Prediction with ML Models about?

The thesis presents a predictive model for child mortality using machine learning techniques, focusing on key factors such as parental age and access to healthcare. It evaluates thirteen classifiers across traditional, tree-based, boosting, and ensemble learning models, with AdaBoost achieving the highest accuracy. The findings suggest that ensemble methods are effective in identifying high-risk populations, aiding policymakers in targeted interventions to reduce child mortality.

Author

sairockers4644

Language

EN