Machine learning models for identifying pre-frailty in community dwelling older adults
The results of this study indicate that machine learning methods are well suited for predicting pre-frailty, and indicate a range of factors that may be useful to include in targeted health assessments to identify pre-frailty in middle aged and older adults.
Predicting Australian adults at high risk of cardiovascular disease mortality using standard risk factors and machine learning
Effective cardiovascular disease (CVD) prevention relies on timely identification and intervention for individuals at risk. Conventional formula-based techniques have been demonstrated to over-or under-predict the risk of CVD in the Australian population. This study assessed the ability of machine learning models to predict CVD mortality...