Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database.

Cholesterol is a crucial risk factor for cardiovascular diseases (CVDs) which in their turn are among the main causes of death worldwide and public health concern, with heart diseases being the most prevalent ones. For cholesterol control, the early prediction is considered one of the most effective ways. Utilizing the English Longitudinal Study of Ageing (ELSA), a large-scale database of ageing participants, a dataset is derived to evaluate the long-term cholesterol risk of elderly men and women using Machine Learning (ML) techniques. Several ML prediction models were assessed concerning Accuracy and Recall where the Logistic model tree was the best performer. The ultimate goal of this study is to identify individuals at risk and facilitate earlier intervention to prevent the future development of cholesterol.