Enhancing dementia prediction models: leveraging temporal patterns and class-balancing methods

Predicting dementia with machine learning (classification) models learned from longitudinal data remains challenging. This paper introduces an innovative approach for learning predictive dementia models that leverage temporal patterns derived from longitudinal data. Specifically, we propose two types of automatically constructed temporal features based on monotonicity patterns of features’ values and decision tree-based patterns. The constructed temporal features were added to the original dataset to improve the predictive performance of well-known classifiers, XGBoost and Random Forest. We also investigated using several types of class-balancing methods to cope with the large degree of imbalanced classes in our dataset. We assessed the impact of the constructed temporal features and different types of class-balancing methods (and their combinations) on improving classifiers’ predictive performance on a dementia dataset derived from the English Longitudinal Study of Ageing. We also report the most important predictive features in the best dementia prediction models learned in our experiments.