Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome

Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome

Abstract Background and objective This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for identifying relevant variables. Second, to validate machine learning models that predict the risk of complications in patients with acute myeloid...

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Main Authors: Pedro Pons-Suñer, François Signol, Noemi Alvarez, Claudia Sargas, Sara Dorado, Jose Vicente Gil Ortí, Juan A. Delgado Sanchis, Marta Llop, Laura Arnal, Rafael Llobet, Juan-Carlos Perez-Cortes, Rosa Ayala, Eva Barragán
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Acute myeloid leukemia
Machine learning
Patient evolution
Therapy outcome
Recurrence
Mortality
Online Access:https://doi.org/10.1186/s12911-025-03001-y
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https://doi.org/10.1186/s12911-025-03001-y

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