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Middle East Journal of Cancer، جلد ۱۴، شماره ۳، صفحات ۳۷۸-۳۸۵
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عنوان انگلیسی Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Data mining, Allogenic hematopoietic stem cell transplantation, Leukemia, Myeloid, Acute
نویسندگان مقاله Mostafa Langarizadeh |
Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

Boshra Farajollahi |
Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

Abbas Hajifathali |
Hematopoietc Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

نشانی اینترنتی https://mejc.sums.ac.ir/article_48319_88d17750f389e64d5b363e4aecd7aefa.pdf
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