k-Prototype Clustering
Cross-source consensus on k-Prototype Clustering from 1 sources and 4 claims.
1 sources · 4 claims
Uses
How it works
Evidence quality
Highlighted claims
- The k-prototype algorithm stratifies patients using natural, health, and behavioural attributes. — Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study
- k-prototype clustering handles mixed numerical and categorical data using Euclidean and Hamming distances. — Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study
- Cluster quality is evaluated using silhouette coefficient, Elbow method, UMAP visualisation, and Adjusted Rand Index stability checks. — Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study
- Biomarkers are included because they can reflect stroke clinical status or evolution and complement clinical endpoints. — Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study