Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study.
Zahra MehrbakhshRoghayyeh HassanzadehNasser BehnampourLeili TapakZiba ZarrinSalman KhazaeiIrina DinuPublished in: BMC medical informatics and decision making (2024)
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.
Keyphrases
- prognostic factors
- machine learning
- neural network
- acute lymphoblastic leukemia
- deep learning
- artificial intelligence
- big data
- climate change
- risk factors
- chronic kidney disease
- end stage renal disease
- cardiovascular disease
- allogeneic hematopoietic stem cell transplantation
- acute myeloid leukemia
- replacement therapy