Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia

Sun Apr 21 00:38:48 2024

(2022) Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia. Bmc Medical Informatics and Decision Making. p. 13.

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Introduction Chronic myeloid leukemia (CML) is a myeloproliferative disorder resulting from the translocation of chromosomes 19 and 22. CML includes 15-20 of all cases of leukemia. Although bone marrow transplant and, more recently, tyrosine kinase inhibitors (TKIs) as a first-line treatment have significantly prolonged survival in CML patients, accurate prediction using available patient-level factors can be challenging. We intended to predict 5-year survival among CML patients via eight machine learning (ML) algorithms and compare their performance. Methods The data of 837 CML patients were retrospectively extracted and randomly split into training and test segments (70:30 ratio). The outcome variable was 5-year survival with potential values of alive or deceased. The dataset for the full features and important features selected by minimal redundancy maximal relevance (mRMR) feature selection were fed into eight ML techniques, including eXtreme gradient boosting (XGBoost), multilayer perceptron (MLP), pattern recognition network, k-nearest neighborhood (KNN), probabilistic neural network, support vector machine (SVM) (kernel = linear), SVM (kernel = RBF), and J-48. The scikit-learn library in Python was used to implement the models. Finally, the performance of the developed models was measured using some evaluation criteria with 95 confidence intervals (CI). Results Spleen palpable, age, and unexplained hemorrhage were identified as the top three effective features affecting CML 5-year survival. The performance of ML models using the selected-features was superior to that of the full-features dataset. Among the eight ML algorithms, SVM (kernel = RBF) had the best performance in tenfold cross-validation with an accuracy of 85.7, specificity of 85, sensitivity of 86, F-measure of 87, kappa statistic of 86.1, and area under the curve (AUC) of 85 for the selected-features. Using the full-features dataset yielded an accuracy of 69.7, specificity of 69.1, sensitivity of 71.3, F-measure of 72, kappa statistic of 75.2, and AUC of 70.1. Conclusions Accurate prediction of the survival likelihood of CML patients can inform caregivers to promote patient prognostication and choose the best possible treatment path. While external validation is required, our developed models will offer customized treatment and may guide the prescription of personalized medicine for CML patients.

Item Type: Article
Shanbehzadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: Machine learning Data mining Support vector machine Leukemia Survival cancer Medical Informatics
Page Range: p. 13
Journal or Publication Title: Bmc Medical Informatics and Decision Making
Journal Index: ISI
Volume: 22
Number: 1
Identification Number:
Depositing User: مهندس مهدی شریفی

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