Author: Hrimov, Andrew; Meniailov, Ievgen; Chumachenko, Dmytro; Bazilevych, Kseniia; Chumachenko, Tetyana
Title: Classification of Diabetes Disease Using Logistic Regression Method Cord-id: dzfm9fdj Document date: 2020_12_4
ID: dzfm9fdj
Snippet: At the moment, there are many methods of analysis and classification aimed at building the most accurate and effective mathematical models that are widely used in medicine as a decision-making tool. Existing methods make it possible to identify the relationships between input and output variables in the sample, build models reflecting these relationships, compare them in terms of accuracy, profitability and costs, and choose the most effective model. The increase in the incidence of diabetes not
Document: At the moment, there are many methods of analysis and classification aimed at building the most accurate and effective mathematical models that are widely used in medicine as a decision-making tool. Existing methods make it possible to identify the relationships between input and output variables in the sample, build models reflecting these relationships, compare them in terms of accuracy, profitability and costs, and choose the most effective model. The increase in the incidence of diabetes not only in the world, but also in Ukraine, dictates the need to introduce a mathematical apparatus for automatic diagnosis of the disease. Within the framework of the study, the classification of patients with diabetes by the logistic regression method was implemented. Python is used for software implementation.
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