ArticlePloS one2021
Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.Pediatric research · 2025Pooled it
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- Acute myeloid leukemia risk stratification in younger and older patients through transcriptomic machine learning models.Scientific reports · 2025Article
- Synthetic Tabular Data Generation Under Horizontal Federated Learning Environments in Acute Myeloid Leukemia: Case-Based Simulation Study.JMIR medical informatics · 2025Article
- Article
- Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome.BMC medical informatics and decision making · 2025Article
- Development and application of machine learning models for hematological disease diagnosis using routine laboratory parameters: a user-friendly diagnostic platform.Frontiers in medicine · 2025Article
- Article
- Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning.Frontiers in pharmacology · 2024Article
- Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury.CNS neuroscience & therapeutics · 2023Article
- Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia.BMC medical informatics and decision making · 2022Article
- Evaluating the frequency, prognosis and survival of RUNX1 and ASXL1 mutations in patients with acute myeloid leukaemia in northeastern Iran.Journal of cellular and molecular medicine · 2022Article
- Survival prediction in acute myeloid leukemia using gene expression profiling.BMC medical informatics and decision making · 2022Article
- Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis.Computational and mathematical methods in medicine · 2022Article
- Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia.Frontiers in oncology · 2022Article
- Durable remission of acute myeloid leukemia in an elderly patient following a limited course of azacitidine and venetoclax.Leukemia research reports · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.