This paper is in the following e-collection/theme issue:

Data Science (270) Innovations and Technology in Cancer Care (423) Demographics of Users, Social & Digital Divide (739)

Published on in Vol 24, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29056, first published .
Use of Multiple Correspondence Analysis and K-means to Explore Associations Between Risk Factors and Likelihood of Colorectal Cancer: Cross-sectional Study

Use of Multiple Correspondence Analysis and K-means to Explore Associations Between Risk Factors and Likelihood of Colorectal Cancer: Cross-sectional Study

Use of Multiple Correspondence Analysis and K-means to Explore Associations Between Risk Factors and Likelihood of Colorectal Cancer: Cross-sectional Study

Authors of this article:

Dídac Florensa1, 2 Author Orcid Image ;   Jordi Mateo-Fornés1 Author Orcid Image ;   Francesc Solsona1 Author Orcid Image ;   Teresa Pedrol Aige3 Author Orcid Image ;   Miquel Mesas Julió2 Author Orcid Image ;   Ramon Piñol4 Author Orcid Image ;   Pere Godoy5, 6, 7 Author Orcid Image
Article Authors Cited by (6) Tweetations (8) Metrics

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  1. Beuken M, Kanera I, Ezendam N, Braun S, Zoet M. Identification and Potential Use of Clusters of Patients With Colorectal Cancer and Patients With Prostate Cancer in Clinical Practice: Explorative Mixed Methods Study. JMIR Cancer 2022;8(4):e42908 View
  2. Gallos I, Tryfonopoulos D, Shani G, Amditis A, Haick H, Dionysiou D. Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions. Diagnostics 2023;13(24):3673 View
  3. Yang W, Lai J, Liu Y, Lin Y, Hou H, Pai P. Using Medical Data and Clustering Techniques for a Smart Healthcare System. Electronics 2023;13(1):140 View
  4. Perthame E, Chartier L, George J, Varloud M, Ferquel E, Choumet V. Case presentation and management of Lyme disease patients: a 9-year retrospective analysis in France. Frontiers in Medicine 2024;10 View
  5. Seghieri C, Tortù C, Tricò D, Leonetti S. Learning prevalent patterns of co-morbidities in multichronic patients using population-based healthcare data. Scientific Reports 2024;14(1) View
  6. Kramer R, Rhodin K, Therien A, Raman V, Eckhoff A, Thompson C, Tong B, Blazer D, Lidsky M, D’Amico T, Nussbaum D. Unsupervised clustering using multiple correspondence analysis reveals clinically-relevant demographic variables across multiple gastrointestinal cancers. Surgical Oncology Insight 2024;1(1):100009 View