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Association of Cardiovascular Events and Blood Pressure and Serum Lipoprotein Indicators Based on Functional Data Analysis as a Personalized Approach to the Diagnosis

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Software Engineering Perspectives in Intelligent Systems (CoMeSySo 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1295))

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Abstract

The development of trends and practice-oriented approaches to personalized programs for the diagnosis and correction depending on the clinical and phenotypic variants of the person is relevant. A software application was created for data mining from respondent profiles in a semi-automatic mode; libraries with data preprocessing were analyzed. The anthropometric measurements and serum lipoprotein spectrum of 2131 volunteers (average age 45.75 ± 11.7 years) were studied. To estimate the association of blood pressure and cardiovascular events markers was carried out by means of multivariate analysis of data by the methods of selection and classification significant signs. The machine learning was used to predict cardiovascular events. Depends on gender there was found the significant difference in atherogenic index of plasma (AIP) (F < 0.05). In young women (20–30 y.o.), the lipoproteins did not correlate with the presence of hypertension, whereas for older women the statistically significant markers were higher, such as cholesterol (CH, F = 0.03), low-density lipoproteins (LDL, F = 0.03) and AIP (F = 0.02). In men for identifying the risk of hypertension developing lipoproteins should be considered depending on age. Accuracy of the risk recognition for the cardiovascular disease (CVD) model was more than 89% with an average confidence of the model in each forecasted case of 90%. The markers for diagnosing the risk of CVD, the following indicators can be used according to their degree of significance: AIP, CH and LDL. Thus, the data obtained indicate the importance of risk factor phenotyping using anthropometric markers and biochemical profile for determining their significance in the top 17 predictors of CVD. The machine learning provides CVD prediction according to standard risk assessments.

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Acknowledgments

The study was supported by a grant from the Russian Foundation for Basic Research 19-29-01077 and is part of the Ministry Health the Russian Federation state task «Clinical and phenotypic variants and molecular genetic features of vascular aging in people of different ethnic groups».

Declaration of financial and other relationships. All authors participated in the development of the concept, the design of the study and the writing of the manuscript. The final version of the manuscript was approved by all authors.

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Authors and Affiliations

  1. Pacific State Medical University, 2 Ostryakova Ave, Vladivostok, 690002, Russia

    N. G. Plekhova, V. A. Nevzorova, T. A. Brodskay, L. G. Priseko, I. N. Chernenko & K. L. Grunberg

  2. Institute of Chemistry, Far Eastern Branch of the Russian Academy of Sciences, 159 Pr. 100th Anniversary of Vladivostok, Vladivostok, 690022, Russia

    N. G. Plekhova & V. A. Nevzorova

  3. Vladivostok State University of Economics and Service, 41 Gogolya St., Vladivistok, 690014, Russia

    K. I. Shakhgeldyan & B. I. Geltser

  4. Far East Federal University, Sukhanova St. 8, 690091, Vladivostok, Russia

    K. I. Shakhgeldyan & B. I. Geltser

Authors
  1. N. G. Plekhova
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  2. V. A. Nevzorova
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  3. T. A. Brodskay
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  4. K. I. Shakhgeldyan
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  6. L. G. Priseko
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  7. I. N. Chernenko
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  8. K. L. Grunberg
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Corresponding author

Correspondence to N. G. Plekhova .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

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© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Plekhova, N.G. et al. (2020). Association of Cardiovascular Events and Blood Pressure and Serum Lipoprotein Indicators Based on Functional Data Analysis as a Personalized Approach to the Diagnosis. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1295. Springer, Cham. https://doi.org/10.1007/978-3-030-63319-6_24

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