Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study
Our results showed that artificial neural networks and bagging algorithms outperformed other algorithms in predicting mortality, while boosting and random forest algorithms excelled in predicting relapse in ALL patients across all criteria. These results offer significant clinical insights into the prognostic factors for children with ALL, which can inform treatment decisions and improve patient outcomes.
Authors
- Irina A Dinu (ORCID: https://orcid.org/0000-0001-6925-623X)
- Leili Tapak (ORCID: https://orcid.org/0000-0002-4378-3143)
- Nasser Behnampour (ORCID: https://orcid.org/0000-0003-1668-8922)
- Salman Khazaei (ORCID: https://orcid.org/0000-0001-5918-2310)
- Roghayyeh Hassanzadeh (ORCID: https://orcid.org/0000-0003-0715-6916)
- Zahra Mehrbakhsh (ORCID: https://orcid.org/0000-0001-8937-644X)
- Ziba Zarrin
Institutions
- Hamedan University of Medical Sciences (IR)
- University of Alberta (CA)
- Golestan University (IR)
- Golestan University of Medical Sciences (IR)
- K. N. Toosi University of Technology (IR)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1186/s12911-024-02645-6
- Citations
- 9
- Primary Topic
- Acute Lymphoblastic Leukemia research
- Type
- article
- Field-Weighted Citation Impact
- 4.89
Funders
- Hamadan University of Medical Sciences