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Erschienen in: Neural Computing and Applications 5/2019

27.04.2018 | S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing

Optimal body mass index cutoff point for cardiovascular disease and high blood pressure

verfasst von: Gokulnath Chandra Babu, S. P. Shantharajah

Erschienen in: Neural Computing and Applications | Ausgabe 5/2019

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Abstract

Increase in body mass index value is a serious health problem across the world. Humans with high body mass index value have the higher probability of getting the cardiovascular disease and high blood pressure. The proposed work is concerned in predicting the probability of CVD and high blood pressure in India. The disease has been predicted with body mass index value; from the health reports of India, the pervasiveness of CVD and HBP is identified. The demographic health survey 2016 of India is used in this work. Bayesian model is used to find the pervasiveness of CVD and HBP based on the gender and the place of living. Out of 432 articles studied, 34 articles suggested the pervasiveness of CVD and HBP. Pervasiveness of CVD increased (95% of interval) rapidly between 1992 and 2016 from 3.0% (0.3–5.7) to 16.4% (9.4–16.2) for men, and from 5.2% (2.3–8.6) to 15.4% (3.3–18.6) for women. Pervasiveness of high blood pressure increased rapidly between 1992 and 2016 from 11.0% (8.6–17.4) to 21.4% (19.4–23.6) for men and from 14.0% (4.3–16.7) to 20.4% (5.6–25.6) for women. The pervasiveness of CVD in 2030 is predicted as 24.6% (13.6–37.8), and pervasiveness of high blood pressure is predicted to be 21.7% (19.6–27.8). The annual average of pervasiveness of CVD is high for women in village areas, and pervasiveness of high blood pressure is found high for men in city regions. The cutoff point for pervasiveness of CVD for overall population is 23.02 kg/m2.

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Metadaten
Titel
Optimal body mass index cutoff point for cardiovascular disease and high blood pressure
verfasst von
Gokulnath Chandra Babu
S. P. Shantharajah
Publikationsdatum
27.04.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3484-3

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