Back to Search
Start Over
Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques
- Source :
- PLoS ONE, Vol 16, Iss 7, p e0254976 (2021), PLoS ONE
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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.
- Subjects :
- Male
Computer science
computer.software_genre
Logistic regression
Biochemistry
Hematologic Cancers and Related Disorders
Machine Learning
0302 clinical medicine
Risk Factors
Breast Tumors
Medicine and Health Sciences
Data Mining
Data Management
Aged, 80 and over
0303 health sciences
Leukemia
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Hematology
Middle Aged
Myeloid Leukemia
Random forest
Survival Rate
Leukemia, Myeloid, Acute
Tree (data structure)
Oncology
Creatinine
030220 oncology & carcinogenesis
Physical Sciences
Medicine
Female
Algorithms
Research Article
Acute Myeloid Leukemia
Adult
Computer and Information Sciences
Adolescent
Science
Decision tree
Feature selection
Research and Analysis Methods
Machine learning
Models, Biological
Disease-Free Survival
Cytogenetics
Machine Learning Algorithms
03 medical and health sciences
Naive Bayes classifier
Artificial Intelligence
Predictive Value of Tests
Albumins
Breast Cancer
Genetics
Humans
Survival rate
Aged
030304 developmental biology
business.industry
Biology and Life Sciences
Cancers and Neoplasms
Proteins
Artificial intelligence
business
computer
Mathematics
Biomarkers
Kappa
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....11fcc2b0f223ecbe888fa3119e00a19f