Intelligent data analysis in biomedical research: Classification trees
Narkevich A.N. Vinogradov K.A. Grjibovski A.M.
2021Northern State Medical University
Ekologiya Cheloveka (Human Ecology)
2021#2021Issue 354 - 64 pp.
Modern analytical tasks in biomedical research require increasingly sophisticated methods of data analysis. In recent years, the term data analysis is not only related to classical statistical tests for hypothesis testing and correlation analysis for studying associations between variables. Classification tree or decision tree analysis is getting more and more frequently used in biomedical research. In this paper we present the use of classification trees in biomedical research and provide examples of their construction in the most commonly used statistical programs. The article is constructed as a problem solving exercise using classification trees with an example of a data set for creation of classification trees and description of how to build a classification tree model in IBM SPSS Statistics and StatSoft Statistica software. Moreover, we provide recommendations on how the results of this analysis should be presented in a scientific article. The use of the classification trees has a potential to contribute to better understanding of the factors behind the observed phenomena in medicine and biology.
Artificial intelligence , Classification trees , Decision trees , Machine learning , Mathematical modeling , SPSS , Statistica
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Voino-Yasenetsky Krasnoyarsk State Medical University, Krasnoyarsk, Russian Federation
Northern State Medical University, Arkhangelsk, Russian Federation
West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
M. K. Ammosov North-Eastern Federal University, Yakutsk, Russian Federation
Voino-Yasenetsky Krasnoyarsk State Medical University
Northern State Medical University
West Kazakhstan Marat Ospanov Medical University
Al-Farabi Kazakh National University
M. K. Ammosov North-Eastern Federal University
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