@article{article_1796696, title={Optimization of the Anemia Diagnosis in Children and Adolescents Using Support Vector Machines}, journal={Hendese Teknik Bilimler ve Mühendislik Dergisi}, volume={2}, pages={84–88}, year={2025}, DOI={10.5281/zenodo.17474575}, author={Korhan, Nuri}, keywords={DVM, Öznitelik seçimi, Genetik algoritma, Fisher puanı, Anemi}, abstract={Support Vector machines (SVMs) are widely used learning methods that often achieve remarkable results, encouraging further research into their applications. This paper presents a paradigm based on classification via SVMs for diagnosing Anemia in children and adolescents (people under 18). As training and test data, hemogram test results of 50 individuals (either patients or healthy) are used. Input data consists of five different features (HGB, HCT, MCV, MCH, and MCHC). In order to increase the classifier efficiency, feature subset selection is applied, and the number of features is decreased. The Fisher Score Algorithm is utilized to obtain the most important features for this preprocessing step. These selected features were then used to train the SVM. After repeated training sessions, it has been observed that the performance depends heavily on not only the input’s selected feature subsets but also the SVM’s hyperparameters. To improve performance (in terms of accuracy), the penalization coefficient of the slack variable is optimized by a well-known optimization method called "Genetic Algorithm".}, number={2}, publisher={Muş Alparslan Üniversitesi}