Machine Learning for Mortality Risk Prediction: A Study on Cohort Data
This thesis explores the application of machine learning techniques to predict mortality risk using data from the Swedish Adoption/Twin Study of Aging (SATSA). Through the development and evaluation of multiple supervised learning models, including Random Forest and Histogram Gradient Boosting classifiers, the study examines mortality predictors across 10-year and 20-year timeframes. The models ac
