title: An approach for leukemia classification based on cooperative game theory.
abstract: Hematological malignancies are the types of cancer that affect blood, bone marrow and lymph nodes. As these tissues are naturally connected through the immune system, a disease affecting one of them will often affect the others as well. The hematological malignancies include; Leukemia, Lymphoma, Multiple myeloma. Among them, leukemia is a serious malignancy that starts in blood tissues especially the bone marrow, where the blood is made. Researches show, leukemia is one of the common cancers in the world. So, the emphasis on diagnostic techniques and best treatments would be able to provide better prognosis and survival for patients. In this paper, an automatic diagnosis recommender system for classifying leukemia based on cooperative game is presented. Through out this research, we analyze the flow cytometry data toward the classification of leukemia into eight classes. We work on real data set from different types of leukemia that have been collected at Iran Blood Transfusion Organization (IBTO). Generally, the data set contains 400 samples taken from human leukemic bone marrow. This study deals with cooperative game used for classification according to different weights assigned to the markers. The proposed method is versatile as there are no constraints to what the input or output represent. This means that it can be used to classify a population according to their contributions. In other words, it applies equally to other groups of data. The experimental results show the accuracy rate of 93.12%, for classification and compared to decision tree (C4.5) with (90.16%) in accuracy. The result demonstrates that cooperative game is very promising to be used directly for classification of leukemia as a part of Active Medical decision support system for interpretation of flow cytometry readout. This system could assist clinical hematologists to properly recognize different kinds of leukemia by preparing suggestions and this could improve the treatment of leukemic patients.
authors: Department of Information Technology, Tarbiat Modarres University, Tehran, Iran.
authors: Atefeh
authors: A
authors: Torkaman
authors: Nasrollah Moghaddam
authors: NM
authors: Charkari
authors: Mahnaz
authors: M
authors: Aghaeipour
mesh_terms: D000465:Algorithms; D000951:Antigens, Neoplasm / Q000032:analysis*; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D003661:Decision Support Techniques; D005434:Flow Cytometry; D005716:Game Theory; D006801:Humans; D016130:Immunophenotyping / Q000379:methods; D007938:Leukemia / Q000145:classification* / Q000175:diagnosis / Q000276:immunology / Q000628:therapy; D011379:Prognosis; D009626:Terminology as Topic
chemical_list: D000951:Antigens, Neoplasm; D014408:Biomarkers, Tumor