Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia.
Background: Allogenic hematopoietic stem cell transplantation is considered as an effective treatment for patients with acute myeloid leukemia. However, complications of transplantation, like aGVHD, affect the efficiency of allogenic hematopoietic stem cell transplantation. The present study aimed t...
| Published in: | Middle East Journal of Cancer Vol. 14; no. 3; pp. 378 - 386 |
|---|---|
| Main Authors: | , , |
| Format: | algorithm tables/charts Journal Article |
| Published: |
Middle East Journal of Cancer
Jul2023
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164699961&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164699961 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20086709 B7LE jtl: Middle East Journal of Cancer issn: 20086709 maglogo: N pubinfo: dt: Jul2023 vid: 14 iid: 3 pid: 67247 pub: Middle East Journal of Cancer artinfo: ui: 164699961 164699961 164699961 10.30476/mejc.2022.94116.1715 164699961 ppf: 378 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia. aug: au: Langarizadeh, Mostafa Farajollahi, Boshra Hajifathali, Abbas affil: Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran sug: subj: Leukemia, Myeloid, Acute Surgery Allografts Hematopoietic Stem Cell Transplantation Graft Versus Host Disease Complications Prediction Models Data Mining Human Adult Hospitals Iran Iran Decision Trees Sensitivity and Specificity Descriptive Statistics Funding Source Adult: 19-44 years ab: Background: Allogenic hematopoietic stem cell transplantation is considered as an effective treatment for patients with acute myeloid leukemia. However, complications of transplantation, like aGVHD, affect the efficiency of allogenic hematopoietic stem cell transplantation. The present study aimed to implement different models of data mining (DM) (single and ensemble) for prediction of allogenic hematopoietic stem cell transplantation in patients with acute myeloid leukemia (transplantation against host disease). Method: We conducted this developmental study on 94 patients with 34 attributes in Taleghani Hospital, Tehran, Iran, during 2009--2017. In this practical study, data were analyzed via decision tree (DT) algorithms, including decision tree, random forest and gradient boosting (ensemble learning), artificial neural network (Single Learning), and support vector machine. Some criteria, like specificity, accuracy, Fmeasure, AUC (area under curve), and sensitivity, were reported in order to evaluate DT algorithms. Results: There were 34 transplantation-related variables; some predictors, such as liver, hemoglobin, and donor blood group, were found to be the most important ones. To predict aGVHD, the two selected algorithms included the most appropriate DM models, artificial neural network and support vector machine classifiers, with ROC of 100. Conclusion: This study indicated that DT algorithms could be successfully used for approving the efficiency of the models predicting allogenic hematopoietic stem cell transplantation. pubtype: Academic Journal doctype: algorithm tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|