Predicting the Onset of Dementia in Initially Healthy Individuals Using Demographic and Clinical Data

In this study, we aim to predict health outcomes concerning dementia by leveraging data from individuals' first two doctor visits. Using the OASIS-2 dataset, we focus on early-stage, longitudinal data, which is especially crucial for patients initially diagnosed as healthy. This approach allows us to anticipate their future health trajectories more accurately. We propose and evaluate multiple machine learning models, finding the Extreme Gradient Boosting (XGBoost) algorithm to be particularly effective, with an accuracy rate of 100%. Our methodology provides a valuable resource for early interventions and preventive measures in dementia care.