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The Prediction of Hypertension Risk

Author

Listed:
  • Massaro, Alessandro
  • Giardinelli, Vito O. M.
  • Cosoli, Gabriele
  • Magaletti, Nicola
  • Leogrande, Angelo

Abstract

This article presents an estimation of the hypertension risk based on a dataset on 1007 individuals. The application of a Tobit Model shows that “Hypertension” is positively associated to “Age”, “BMI-Body Mass Index”, and “Heart Rate”. The data show that the element that has the greatest impact in determining inflation risk is “BMI-Body Mass Index”. An analysis was then carried out using the fuzzy c-Means algorithm optimized with the use of the Silhouette coefficient. The result shows that the optimal number of clusters is 9. A comparison was then made between eight different machine-learning algorithms for predicting the value of the Hypertension Risk. The best performing algorithm is the Gradient Boosted Trees Regression according to the analyzed dataset. The results show that there are 37 individuals who have a predicted hypertension value greater than 0.75, 35 individuals who have a predicted hypertension value between 0.5 and 0.75, while 227 individuals have a hypertension value between 0.0 and 0.5 units.

Suggested Citation

  • Massaro, Alessandro & Giardinelli, Vito O. M. & Cosoli, Gabriele & Magaletti, Nicola & Leogrande, Angelo, 2022. "The Prediction of Hypertension Risk," MPRA Paper 113242, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:113242
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    File URL: https://mpra.ub.uni-muenchen.de/113242/1/MPRA_paper_113242.pdf
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    References listed on IDEAS

    as
    1. Lerner, Joshua & Stern, Scott (ed.), 2018. "Innovation Policy and the Economy, 2017," National Bureau of Economic Research Books, University of Chicago Press, number 9780226576060, October.
    2. Louise A C Millard & Neil M Davies & Kate Tilling & Tom R Gaunt & George Davey Smith, 2019. "Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization," PLOS Genetics, Public Library of Science, vol. 15(2), pages 1-20, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Predictions; Machine Learning Algorithms; Correlation Matrix; Tobit Model; Fuzzy c-Means Clustering.;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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