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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.15882 |
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| _version_ | 1866917674665115648 |
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| author | Calitis, Mikayla |
| author_facet | Calitis, Mikayla |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15882 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques Calitis, Mikayla Machine Learning Artificial Intelligence 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. |
| title | Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.15882 |