Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques
- URL: http://arxiv.org/abs/2405.15882v1
- Date: Fri, 24 May 2024 18:53:28 GMT
- Title: Risk Factor Identification In Osteoporosis Using Unsupervised Machine Learning Techniques
- Authors: Mikayla Calitis,
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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