Abstract
Anemia is a pathological disorder of the structure of blood cells, in which the concentration of hemoglobin is reduced. At the same time, the number of red blood cells decreases. Anemia is characterized as a secondary disease and is a symptom of other ailments. Within the framework of this study, a medical algorithm for diagnosing anemia is considered, as well as the results of using machine learning methods to detect pathology are studied. The stages of the implementation of a medical algorithm for diagnosing anemia are described, as well as the results of model training based on the linear support vector method, logistic regression, the k-nearest neighbor method, decision tree, naive Bayesian classifier method, and random forest method.