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Langarizadeh, Mostafa, Farajollahi, Boshra, Hajifathali, Abbas. (1402). Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia. سامانه مدیریت نشریات علمی, 14(3), 378-385. doi: 10.30476/mejc.2022.94116.1715
Mostafa Langarizadeh; Boshra Farajollahi; Abbas Hajifathali. "Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia". سامانه مدیریت نشریات علمی, 14, 3, 1402, 378-385. doi: 10.30476/mejc.2022.94116.1715
Langarizadeh, Mostafa, Farajollahi, Boshra, Hajifathali, Abbas. (1402). 'Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia', سامانه مدیریت نشریات علمی, 14(3), pp. 378-385. doi: 10.30476/mejc.2022.94116.1715
Langarizadeh, Mostafa, Farajollahi, Boshra, Hajifathali, Abbas. Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia. سامانه مدیریت نشریات علمی, 1402; 14(3): 378-385. doi: 10.30476/mejc.2022.94116.1715

Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia

Middle East Journal of Cancer
مقاله 4، دوره 14، شماره 3 - شماره پیاپی 55، مهر 2023، صفحه 378-385    اصل مقاله (860.95 K)
نوع مقاله: Original Article(s)
شناسه دیجیتال (DOI): 10.30476/mejc.2022.94116.1715
نویسندگان
Mostafa Langarizadeh1؛ Boshra Farajollahi* 1؛ Abbas Hajifathali2
1Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran
2Hematopoietc Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
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.

تازه های تحقیق
Mostafa Langarizadeh (Google Scholar)
Boshra Farajollahi (Google Scholar)
کلیدواژه‌ها
Data mining؛ Allogenic hematopoietic stem cell transplantation؛ Leukemia؛ Myeloid؛ Acute
مراجع
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