Version 1
: Received: 30 May 2022 / Approved: 31 May 2022 / Online: 31 May 2022 (13:51:12 CEST)
How to cite:
Massaro, A.; Giardinelli, V.; Cosoli, G.; Magaletti, N.; Leogrande, A. The Prediction of Hypertension Risk. Preprints2022, 2022050418. https://doi.org/10.20944/preprints202205.0418.v1
Massaro, A.; Giardinelli, V.; Cosoli, G.; Magaletti, N.; Leogrande, A. The Prediction of Hypertension Risk. Preprints 2022, 2022050418. https://doi.org/10.20944/preprints202205.0418.v1
Massaro, A.; Giardinelli, V.; Cosoli, G.; Magaletti, N.; Leogrande, A. The Prediction of Hypertension Risk. Preprints2022, 2022050418. https://doi.org/10.20944/preprints202205.0418.v1
APA Style
Massaro, A., Giardinelli, V., Cosoli, G., Magaletti, N., & Leogrande, A. (2022). The Prediction of Hypertension Risk. Preprints. https://doi.org/10.20944/preprints202205.0418.v1
Chicago/Turabian Style
Massaro, A., Nicola Magaletti and Angelo Leogrande. 2022 "The Prediction of Hypertension Risk" Preprints. https://doi.org/10.20944/preprints202205.0418.v1
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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
