Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques
This work addresses osteoporosis risk factor identification for medical researchers, but appears incremental as it builds on existing clustering approaches with some methodological refinements.
The study tackled the problem of identifying reliable osteoporosis risk factors by developing a new clustering-based method called CLIF that incorporates Wasserstein distance and ANOVA/ablation tests. The method validated some existing risk factors while weakening the reliability of others, though no concrete numerical results were provided.
In this study, the reliability of identified risk factors associated with osteoporosis is investigated using a new clustering-based method on electronic medical records. This study proposes utilizing a new CLustering Iterations Framework (CLIF) that includes an iterative clustering framework that can adapt any of the following three components: clustering, feature selection, and principal feature identification. The study proposes using Wasserstein distance to identify principal features, borrowing concepts from the optimal transport theory. The study also suggests using a combination of ANOVA and ablation tests to select influential features from a data set. Some risk factors presented in existing works are endorsed by our identified significant clusters, while the reliability of some other risk factors is weakened.