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Application of medical data classification methods for a medical decision support system
Decision support systems (DSS) allow us to help the doctor in making diagnoses to the patient, also medical DSS help to assess the need for a particular examination of the patient. In this article methods of medical data classification are considered, these methods are the part of the medical DSS. The paper includes investigation of data classification methods as hierarchical cluster analysis, k-means analysis and discriminant analysis. The selected methods are implemented using the example of cardiological data. A hypothesis is put forward that it is possible to determine the presence or absence of tuberculosis in a person from cardiological data by using data classification methods. Such indicators as sensitivity and specificity evaluate the effectiveness of the methods. In addition, ROC and AUC are presented. Thus, the DSS will be able to determine a certain degree of probability to assume the presence of tuberculosis in a person. The doctor will decide on the need for additional examinations depending on the values obtained.