Explainable AI for Fertility Risk in India

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This project presents an MSc dissertation titled "Explainable Machine Learning for Fertility Risk" by Ankur Kumar Jaiswal from CUH. The study addresses fertility challenges in India's BIMARU states using NFHS-5 data to develop explainable machine learning classifiers. Key methodologies include Optuna and SHAP for explainability, with external validation via Nepal's DHS data. The research identifies significant predictors of fertility risk and suggests policy interventions. It highlights the...