Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques

Mikayla Calitis
5/24/2024
cs.LGcs.AI

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Abstract

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.

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Cite this paper

@article{calitis2024risk,
  title  = {Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques},
  author = {Mikayla Calitis},
  year   = {2024},
  eprint = {2405.15882},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.15882v1}
}

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