DOI 10.17586/0021-3454-2024-67-4-321-329
UDC 004.89
DATA MINING IN THE DIAGNOSIS OF ANEMIA BY CLINICAL INDICATORS
St. Petersburg State University of Aerospace Instrumentation, Department of Applied Informatics; Senior Lecturer
N. Y. Chernysh
V. A. Almazov National Medical Research Center, Department of Laboratory Medicine with Clinic; Associate Professor
M. T. Tatarnikova
Saint Petersburg State University of Aerospace Instrumentation; Professor
Reference for citation: Bozhenko V. V., Chernysh N. Yu., Tatarnikova T. M. Data mining in the diagnosis of anemia by clinical indicators. Journal of Instrument Engineering. 2024. Vol. 67, N 4. P. 321—329 (in Russian). DOI: 10.17586/0021-3454-2024-67-4-321-329.
Abstract. A set of medical data obtained from the information system of a network laboratory for outpatient observation, which contains test indicators of patients diagnosed with anemia, is studied. The set contains indicators of a general blood test, reticulocytes, additional biochemical markers of iron metabolism and the inflammatory process. A program is developed to automate the process of analyzing the test set according to the proposed processing algorithm, taking into account the medical data characteristics. Preliminary preparation and data cleaning are completed, statistical and factor analysis are carried out. Analysis of the selected groups of data makes it possible to find some common indicators for patients with anemic syndrome. Using factor analysis, the number of variables is reduced and four main factors (groups of initial characteristics) necessary to describe the data under study are identified. The results obtained can be used to provide static reports to a medical organization. Also, the studied data are prepared to allow the use of machine learning methods and deeper analysis in order to identify the most effective diagnosis of anemia in the early stages.
Abstract. A set of medical data obtained from the information system of a network laboratory for outpatient observation, which contains test indicators of patients diagnosed with anemia, is studied. The set contains indicators of a general blood test, reticulocytes, additional biochemical markers of iron metabolism and the inflammatory process. A program is developed to automate the process of analyzing the test set according to the proposed processing algorithm, taking into account the medical data characteristics. Preliminary preparation and data cleaning are completed, statistical and factor analysis are carried out. Analysis of the selected groups of data makes it possible to find some common indicators for patients with anemic syndrome. Using factor analysis, the number of variables is reduced and four main factors (groups of initial characteristics) necessary to describe the data under study are identified. The results obtained can be used to provide static reports to a medical organization. Also, the studied data are prepared to allow the use of machine learning methods and deeper analysis in order to identify the most effective diagnosis of anemia in the early stages.
Keywords: medical data analysis, machine learning, medical statistics, clinical indicators, descriptive statistics, factor analysis
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