Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques
<|reference_start|>Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques: 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.<|reference_end|>
@article{calitis2024risk,
    title={Risk Factor Identification In Osteoporosis Using Unsupervised Machine
  Learning Techniques},
    author={Mikayla Calitis},
    journal={arXiv preprint arXiv:2405.15882},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15882},
    primaryClass={cs.LG cs.AI}
}