Suicide Prediction from Hemogram with Machine Learning

dc.contributor.authorArı, Berna
dc.contributor.authorArı, Ali
dc.contributor.authorSengur, Abdulkadir
dc.date.accessioned2026-08-12T15:34:30Z
dc.date.issued2020
dc.departmentFırat Üniversitesi
dc.description.abstractSuicide; It is a phenomenon that we encounter with different frequencies and methods by hosting social, economic and cultural factorsat its base. Adolescence, which is an upper step of childhood, contains complex emotions such as hopelessness, loneliness, anddepression in its world, and it is a stage in which the risk of suicide is high. It is of great importance to take necessary measures inneutral and imperceptible ways in terms of adolescence and suicide relationship. Blood, which can be easily taken by experts even ina non-severe illness, appears as numerical data with the parametric values that make up its content in laboratories. The hemogram testshowing the measurement of blood parameters is used in the diagnosis of many diseases today. In this study, the relationship betweenthe values obtained as a result of the hemogram test and the possibility of suicide of adolescent individuals were investigated.Leukocyte (WBC), erythrocyte (RBC), basophil (BA), eosinophil (EO), lymphocyte (LY), Monocyte (MO), Neutrophil (NE) andPlatelet (PLT) of adolescents who have attempted suicide and whose age and gender are known, blood values of mean platelet volume(MPV) and hemoglobin (HGB) levels were evaluated within the designed system. Complete blood count data of 302 individuals whowere healthy and suicidal attempts constituting the dataset were pre-processed and the data that would adversely affect the estimatedsuicide group were removed from the system by considering the references. While making suicide estimation, the high performancebagging trees and the Support Vector Machines separating the members of the two groups with high accuracy were chosen as a resultof the joint study of the classification algorithms. It has been shown that by using 260x13 attribute, the classification results can beobtained with BT and Quadratic SVM and 93.5% accurate predictions can be made with BT. Experts will be able to easily find outhow high or at which level this probability is, if the individual has any psychological disorders or if the suicide is suspected.
dc.identifier.doi10.31590/ejosat.araconf47
dc.identifier.endpage369
dc.identifier.issn2148-2683
dc.identifier.issueEjosat Özel Sayı 2020 (ARACONF)
dc.identifier.startpage364
dc.identifier.trdizinid364940
dc.identifier.urihttps://doi.org/10.31590/ejosat.araconf47
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/364940
dc.identifier.urihttps://hdl.handle.net/11508/34376
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofEuropan Journal of Science and Technology
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK//
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR-Dizin_20260511
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectPsikoloji
dc.titleSuicide Prediction from Hemogram with Machine Learning
dc.typeArticle

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