Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome
The models achieved performance levels that suggest they could serve as therapeutic decision support tools at different times after diagnosis. The selected variables align with the European LeukemiaNet (ELN) 2022 risk classification, and the SEQENS-based feature selection effectively reduced the feature set while maintaining prediction accuracy.
Authors
- François Signol (ORCID: https://orcid.org/0000-0002-5359-7256)
- Juan-Carlos Pérez-Cortés (ORCID: https://orcid.org/0000-0001-6506-090X)
- Eva Barragán (ORCID: https://orcid.org/0000-0001-6735-0038)
- Claudia Sargas (ORCID: https://orcid.org/0000-0001-8144-7995)
- Laura Arnal (ORCID: https://orcid.org/0000-0002-2173-8886)
- Juan A. Delgado (ORCID: https://orcid.org/0000-0002-4466-4445)
- Marta Llop (ORCID: https://orcid.org/0000-0002-0106-7296)
- Sara Dorado (ORCID: https://orcid.org/0000-0002-4144-0315)
- Rafael Llobet (ORCID: https://orcid.org/0000-0002-8278-9740)
- Pedro Fandos Pons (ORCID: https://orcid.org/0009-0000-3476-295X)
- Rosa María Ayala (ORCID: https://orcid.org/0000-0002-2699-8353)
- José Vicente Gil (ORCID: https://orcid.org/0000-0003-2097-4796)
- Noemí Álvarez (ORCID: https://orcid.org/0000-0002-4891-1917)
Institutions
- Hospital Universitario 12 De Octubre (ES)
- Instituto de Investigación Sanitaria La Fe (ES)
- Universidad Carlos III de Madrid (ES)
- Universitat Politècnica de València (ES)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1186/s12911-025-03001-y
- Citations
- 1
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.28
Funders
- Universitat Politècnica de València