Abstract
Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan–Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.
| Original language | English |
|---|---|
| Pages (from-to) | 2651-2667 |
| Number of pages | 17 |
| Journal | Future Oncology |
| Volume | 19 |
| Issue number | 40 |
| DOIs | |
| Publication status | Published - 14 Dec 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- breast cancer
- cancer risk profiles
- Cox regression
- K-means clustering
- Kaplan–Meier curve
- machine learning
- shiny
- survival
- unsupervised learning
- web-based application
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