The role of correspondence analysis in medical research
Bibliographic Data
| ID | 22086645 |
|---|---|
| Authors | Bojan Žlahtič (0000-0001-9295-7886, University of Maribor), Peter Kokol (0000-0001-9102-9359, University of Maribor, corresponding author), Helena Blažun Vošner (0000-0003-1998-7346, Šolski center Slovenj Gradec), Jernej Završnik (0000-0002-5023-0483, Alma Mater Europaea) |
| Year | 2024 |
| Volume | 12 |
| Pages | 1362699-1362699 |
| Publication date | 2024-03-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1362699 |
| PMID | 38584915 |
| OpenAlex | W4393091064 |
| Language | EN |
| Citations received | 3 |
| References cited | 19 |
Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret “bi–plots,” where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection
Categorical variable · Cluster analysis · Contingency table · Correspondence analysis · Data mining · Dimensionality reduction · Machine learning · Multiple correspondence analysis · Natural language processing · Row · Statistics · Visualization · Bioinformatics and Genomic Networks · Computer Science · Mathematics · Mental Health Research Topics · Sensory Analysis and Statistical Methods · Artificial Intelligence
| Unique citing works | 3 |
|---|---|
| Citations per year | 1.5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |